Datasets:

jamescalam
/

channel-metadata

Dataset card Viewer Files Files and versions Community

Split (1)

train · 1.5k rows

Video ID string	Channel ID string	Title string	Time Created string	Time Published string	Duration string	Description string	Category string	Like Count float64	Dislike Count float64
I3na13AESjw	UCv83tO5cePwHMt1952IVVHw	How to use Color Histograms for Image Retrieval	2022-07-11 07:01:31 UTC	2022-07-13 16:22:08 UTC	1864 seconds	Browsing, searching, and retrieving images has never been easy. Traditionally, many technologies relied on manually appending metadata to images and searching via this metadata. This approach works for datasets with high-quality annotation, but most datasets are too large for manual annotation. That means any large image dataset must rely on Content-Based Image Retrieval (CBIR). Search with CBIR focuses on comparing the content of an image rather than its metadata. Content can be color, shapes, textures – or with some of the latest advances in ML — the "human meaning" behind an image. Color histograms represent one of the first CBIR techniques, allowing us to search through images based on their color profiles rather than metadata. 🌲 Pinecone article: https://pinecone.io/learn/color-histograms 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:23 What are Color Histograms? 08:39 How to Built Color Histograms 16:56 Using OpenCV calcHist 20:36 Image Retrieval 27:37 Pros and Cons 30:40 Final Points	Science & Technology	23	0
UzkdOg7wWmI	UCv83tO5cePwHMt1952IVVHw	🤗 Hugging Face just released Diffusers - for models like DALL-E 2 and Imagen!	2022-07-23 21:33:08 UTC	2022-07-26 15:27:46 UTC	934 seconds	Hugging Face of transformer fame have created a whole new Python library for diffusion models! Diffusion models are a key component of models like OpenAI's DALL-E-2, Google's Imagen, and Midjourney's image generation service. HuggingFace Diffusers brings these models to a new level of accessibility (and open source!). 📕 Article: https://towardsdatascience.com/hugging-face-just-released-the-diffusers-library-846f32845e65 📖 Friend Link (free access): https://towardsdatascience.com/hugging-face-just-released-the-diffusers-library-846f32845e65?sk=9ec4027460defa1fd25178af9a55da13 🧨 Diffusers: https://github.com/huggingface/diffusers 👾 Discord: https://discord.gg/c5QtDB9RAP 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 00:00 What are Diffusers? 01:55 Getting started 04:20 Prompt engineering 09:34 Testing other diffusers	Science & Technology	61	0
szfG55juoJE	UCv83tO5cePwHMt1952IVVHw	How I work from anywhere	2022-07-24 14:01:51 UTC	2022-08-16 13:55:16 UTC	767 seconds	Overview of how I deal with travel and work. Remote desk setup for staying as ergonomic and productive as possible, enjoy! 🔗 Links to products (mostly affiliate): Laptop stand: https://amzn.to/3bZqMHM Second screen: https://amzn.to/3w6IT5B Cable bag (international): https://amzn.to/3QBH7S7 ... or UK: https://amzn.to/3ps5lT2 Peak Design backpacks: https://www.peakdesign.com/products/everyday-backpack 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP	Science & Technology	32	1
jjQetJtQDS4	UCv83tO5cePwHMt1952IVVHw	Bag of Visual Words for Image Classification and Retrieval	2022-08-02 20:39:30 UTC	2022-08-03 13:00:35 UTC	3367 seconds	In computer vision, bag of visual words (BoVW) is one of the pre-deep learning models used for building image embeddings. Allowing us to retrieve images from a database that are similar to another "query" image, perform object detection, and image classification. 🌲 Pinecone article: https://www.pinecone.io/learn/bag-of-visual-words/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP	Science & Technology	33	0
989aKUVBfbk	UCv83tO5cePwHMt1952IVVHw	Fast intro to multi-modal ML with OpenAI's CLIP	2022-08-11 06:17:14 UTC	2022-08-11 13:03:08 UTC	1374 seconds	OpenAI's CLIP is "multi-modal" model capable of understanding the relationships and concepts between both text and images. As we'll see, CLIP is very capable, and when used via the Hugging Face library, could not be easier to work with. 📕 Article: https://towardsdatascience.com/quick-fire-guide-to-multi-modal-ml-with-openais-clip-2dad7e398ac0 📖 Friend Link (free access): https://towardsdatascience.com/quick-fire-guide-to-multi-modal-ml-with-openais-clip-2dad7e398ac0?sk=89bb2d8b8e583ed109d8a05e00366645 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:15 What is CLIP? 02:13 Getting started 05:38 Creating text embeddings 07:23 Creating image embeddings 10:26 Embedding a lot of images 15:08 Text-image similarity search 21:38 Alternative image and text search	Science & Technology	31	0
c_u4AHNjOpk	UCv83tO5cePwHMt1952IVVHw	AlexNet and ImageNet Explained	2022-08-23 22:13:25 UTC	2022-08-24 13:00:22 UTC	2180 seconds	Today’s deep learning revolution traces back to the 30th of September, 2012. On this day, a Convolutional Neural Network (CNN) called AlexNet won the ImageNet 2012 challenge. AlexNet didn’t just win; it dominated. AlexNet was unlike the other competitors. This new model demonstrated unparalleled performance on the largest image dataset of the time, ImageNet. This event made AlexNet the first widely acknowledged, successful application of deep learning. It caught people’s attention with a 9.8 percentage point advantage over the nearest competitor. Until this point, deep learning was a nice idea that most deemed as impractical. AlexNet showed that deep learning was more than a pipedream, and the authors showed the world how to make it practical. Yet, the surge of deep learning that followed was not fueled solely by AlexNet. Indeed, without the huge ImageNet dataset, there would have been no AlexNet. The future of AI was to be built on the foundations set by the ImageNet challenge and the novel solutions that enabled the synergy between ImageNet and AlexNet. 🌲 Pinecone article: https://pinecone.io/learn/imagenet 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:06 Birth of Deep Learning 02:52 ImageNet 07:56 Lack of Readiness for Big Datasets 09:57 ImageNet Challenge (ILSVRC) 11:47 AlexNet 19:30 PYTORCH IMPLEMENTATION 19:55 Data Preprocessing 27:06 Class Prediction with AlexNet 31:50 Goldfish Results 34:27 Closing Notes	Science & Technology	20	0
pfwBut7E60Q	UCv83tO5cePwHMt1952IVVHw	Ultra-efficient Classifier Fine-tuning with Vector Search	2022-08-31 00:32:14 UTC	2022-08-31 13:00:26 UTC	1932 seconds	Learn how to use vector search to create highly targeted training for any classification model using a final linear classification layer. Easily fine-tune models in 10 minutes with less than 100 labeled examples. 🌲 Pinecone article: https://pinecone.io/learn/classifier-train-vector-search/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:14 Classification 02:49 Better Classifier Training 06:33 Classification as Vector Search 08:47 How Fine-tuning Works 10:50 Identifying Important Samples 12:39 CODE IMPLEMENTATION 13:13 Indexing 18:59 Fine-tuning the Classifier 27:37 Classifier Predictions 30:43 Closing Notes	Science & Technology	49	0
-S20nblUuNw	UCv83tO5cePwHMt1952IVVHw	Hugging Face Datasets #1 - Hosting your datasets	2022-09-09 12:52:32 UTC	2022-09-09 14:18:34 UTC	1382 seconds	Introduction to Hugging Face datasets, how it works, and how to host your own simple datasets (JSONL, TSV, CSV, etc) for free via Hugging Face Datasets Hub Warp download: https://app.warp.dev/referral/7G3N39 Git LFS Install: Mac: $ brew install git-lfs Debian/Ubuntu: $ curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \| sudo bash $ sudo apt-get install git-lfs Windows: Get install from https://github.com/git-lfs/git-lfs/releases 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 04:36 Creating our own Datasets 08:29 Creating JSONL for Hugging Face 15:15 Uploading Datasets for Git 19:10 LFS for Large Files 21:56 Closing Notes	Science & Technology	14	0
fGwH2YoQkDM	UCv83tO5cePwHMt1952IVVHw	CLIP Explained \| Multi-modal ML	2022-09-14 23:08:40 UTC	2022-09-15 13:00:22 UTC	2013 seconds	Language models (LMs) can not rely on language alone. That is the idea behind the "Experience Grounds Language" paper, that proposes a framework to measure LMs' current and future progress. A key idea is that, beyond a certain threshold LMs need other forms of data, such as visual input. The next step beyond well-known language models; BERT, GPT-3, and T5 is ”World Scope 3”. In World Scope 3, we move from large text-only datasets to large multi-modal datasets. That is, datasets containing information from multiple forms of media, like both images and text. The world, both digital and real, is multi-modal. We perceive the world as an orchestra of language, imagery, video, smell, touch, and more. This chaotic ensemble produces an inner state, our "model" of the outside world. AI must move in the same direction. Even specialist models that focus on language or vision must, at some point, have input from the other modalities. How can a model fully understand the concept of the word "person" without seeing a person? OpenAI's Contrastive Learning In Pretraining (CLIP) is a world scope three model. It can comprehend concepts in both text and image and even connect concepts between the two modalities. In this video we will learn about multi-modality, how CLIP works, and how to use CLIP for different use cases like encoding, classification, and object detection. 🌲 Pinecone article: https://pinecone.io/learn/clip/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP	Science & Technology	50	1
ODdKC30dT8c	UCv83tO5cePwHMt1952IVVHw	Hugging Face Datasets #2 - Dataset Builder Scripts	2022-09-23 14:06:51 UTC	2022-09-23 14:45:22 UTC	1404 seconds	How to work with dataset builder scripts, intro to the download manager, and Apache Arrow datatypes used in Hugging Face Datasets. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:49 Creating Compressed Files 02:41 Creating Dataset Build Script 04:49 Download Manager 08:59 Finishing Split Generator 10:13 Generate Examples Method 14:47 Add Dataset to Hugging Face 17:49 Apache Arrow Features 22:52 What's Next?	Science & Technology	14	0
98POYg2HZqQ	UCv83tO5cePwHMt1952IVVHw	Zero-Shot Image Classification with OpenAI's CLIP	2022-10-04 05:29:02 UTC	2022-10-05 14:00:03 UTC	1303 seconds	State-of-the-art (SotA) computer vision (CV) models are characterized by a restricted understanding of the visual world specific to their training data [1]. These models can perform very well on specific tasks and datasets, but they do not generalize well. They cannot handle new classes or images beyond the domain they have been trained with. Ideally, a CV model should learn the contents of images without excessive focus on the specific labels it is initially trained to understand. Fortunately, OpenAI's CLIP has proved itself as an incredibly flexible CV classification model that often requires zero retraining. In this chapter, we will explore CLIP in zero-shot image classification. 🌲 Pinecone article: https://pinecone.io/learn/clip-classification/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP	Science & Technology	12	0
Jk1YP4Y_U_0	UCv83tO5cePwHMt1952IVVHw	Stoic Philosophy Text Generation with TensorFlow	2020-04-19 11:33:45 UTC	2020-04-19 13:52:43 UTC	1859 seconds	Explanation of key parts to a RNN text generator built in TensorFlow with Python. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 I've written a couple of Medium articles on this project, if you're interested check them out here: Stoic Philosophy - Built by Algorithms https://towardsdatascience.com/stoic-philosophy-built-by-algorithms-9cff7b91dcbd Supercharged Prediction with Ensemble Learning https://towardsdatascience.com/recurrent-ensemble-learning-caffdcd94092 Music used by Lakey Inspired. 1 - Blue Boi 2 - Falling https://www.youtube.com/channel/UCOmy8wuTpC95lefU5d1dt2Q	People & Blogs	10	0
gXqHd6-NKBo	UCv83tO5cePwHMt1952IVVHw	How to Build TensorFlow Pipelines with tf.data.Dataset	2020-11-02 08:23:38 UTC	2020-11-02 08:57:48 UTC	1853 seconds	Link to updated version (without video freeze): https://youtu.be/f6XVfgJTbp4 An introduction to building better input pipelines for Machine Learning in TF2. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Link to tf.data API docs: https://www.tensorflow.org/guide/data	People & Blogs	46	9
yYEPNla4tlQ	UCv83tO5cePwHMt1952IVVHw	Every New Feature in Python 3.10.0a2	2020-11-08 18:09:49 UTC	2020-11-10 16:44:05 UTC	883 seconds	Every new feature in the early release alpha 2 preview of Python 3.10 There is video lag 5:00 - 9:55 covering the Type Alias section (sorry!) - the audio is okay though 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	People & Blogs	88	5
GYDFBfx8Ts8	UCv83tO5cePwHMt1952IVVHw	How-to Build a Transformer for Language Classification in TensorFlow	2020-11-19 09:57:27 UTC	2020-11-19 12:20:35 UTC	2299 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp How to build a transformer model for sentiment analysis (language classification) using HuggingFace's Transformers library in TensorFlow 2 with Python. We cover the full process from downloading data all the way through to building and training the transformer model. This is a multi-class classification problem using both TensorFlow and Transformers to build a multiclass sentiment classifier. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Article version is here: https://betterprogramming.pub/build-a-natural-language-classifier-with-bert-and-tensorflow-4770d4442d41 Or here (free link if you don't have Medium membership): https://betterprogramming.pub/build-a-natural-language-classifier-with-bert-and-tensorflow-4770d4442d41?sk=346cd4ce5ee019c400835588b56d8574 Article extract: "High-performance transformer models like BERT and GPT-3 are transforming a huge array of previously menial, language-based tasks, into the work of a few clicks, saving a lot of time. In most industries, the newest wave of language optimization is just getting started — taking their first baby steps. But these seedlings are widespread, and sprouting quickly. Much of this adoption is thanks to the incredibly low barrier-to-entry. If you know the basics of TensorFlow or PyTorch, and take a little time to get to grips with the Transformers library — you’re already halfway there. With the Transformers library, it takes just three lines of code to initialize a cutting-edge ML model — a model built from the billions of research dollars spent by the likes of Google, Facebook, and OpenAI. This article will take you through the steps to build a classification model that leverages the power of transformers, using Google’s BERT. Transformers - Finding Models - Initializing - Bert Inputs and Outputs Classification - The Data - Tokenization - Data Prep - Train-Validation Split - Model Definition - Train"	People & Blogs	384	12
DgGFhQmfxHo	UCv83tO5cePwHMt1952IVVHw	How-to use the Kaggle API in Python	2020-11-22 20:19:30 UTC	2020-11-22 20:29:27 UTC	462 seconds	Simple step-by-step tutorial covering the setup and use of the Kaggle API for downloading datasets using the Kaggle library in Python. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	People & Blogs	121	6
YvVQgvAz9dY	UCv83tO5cePwHMt1952IVVHw	Language Generation with OpenAI's GPT-2 in Python	2020-11-23 12:36:44 UTC	2020-11-24 14:22:46 UTC	498 seconds	Easy natural language generation with Transformers and PyTorch. We apply OpenAI's GPT-2 model to generate text in just a few lines of Python code. Language generation is one of those natural language tasks that can really produce an incredible feeling of awe at how far the fields of machine learning and artificial intelligence have come. GPT-1, 2, and 3 are OpenAI’s top language models — well known for their ability to produce incredibly natural, coherent, and genuinely interesting language. In this article, we will take a small snippet of text and learn how to feed that into a pre-trained GPT-2 model using PyTorch and Transformers to produce high-quality language generation in just eight lines of code. We cover: PyTorch and Transformers - Data Building the Model - Initialization - Tokenization - Generation - Decoding Results 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium Article: https://towardsdatascience.com/text-generation-with-python-and-gpt-2-1fecbff1635b Friend Link (free access): https://towardsdatascience.com/text-generation-with-python-and-gpt-2-1fecbff1635b?sk=930367d835f15abb4ef3164f7791e1b1 Thumbnail background by gustavo centurion on Unsplash https://unsplash.com/photos/O6fs4ablxw8	People & Blogs	133	1
egDIqQIjDCI	UCv83tO5cePwHMt1952IVVHw	Text Summarization with Google AI's T5 in Python	2020-11-24 21:26:27 UTC	2020-11-27 06:00:07 UTC	419 seconds	Easy text summarization using Google AI's T5 model using HuggingFace transformers and PyTorch in Python. Automatic text summarization allows us to shorten long pieces of text into easy-to-read, short snippets that still convey the most important and relevant information of the original text. In this video, we’ll build a simple but incredibly powerful text summarizer using Google’s T5. We’ll be using the PyTorch and HuggingFace’s Transformers frameworks. This is split into three parts: 1. Import and Initialization 2. Data and Tokenization 3. Summary Generation 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 You can read the article version of this on Medium here: https://betterprogramming.pub/how-to-summarize-text-with-googles-t5-4dd1ae6238b6 (And for those of you without Medium membership, here's a free link): https://betterprogramming.pub/how-to-summarize-text-with-googles-t5-4dd1ae6238b6?sk=740d3009282cb2c4f7478a0c073dedb3 Thumbnail background by gustavo centurion on Unsplash https://unsplash.com/photos/O6fs4ablxw8	People & Blogs	115	1
DFtP1THE8fE	UCv83tO5cePwHMt1952IVVHw	How-to do Sentiment Analysis with Flair in Python	2020-12-04 11:15:10 UTC	2020-12-04 14:00:03 UTC	848 seconds	Learn how to perform powerful sentiment analysis with no fine-tuning or pre-training required using the Flair NLP library in Python. With the real-time information available to us on massive social media platforms like Twitter, we have all the data we could ever need to create these accurate and up-to-date sentiment metrics for different companies. But then comes the question, how can our computer understand what this unstructured text data means? That is where sentiment analysis comes in. Sentiment analysis is a particularly interesting branch of Natural Language Processing (NLP), which is used to rate the language used in a body of text. Through sentiment analysis, we can take thousands of tweets about a company and judge whether they are generally positive or negative (the sentiment) in real-time! 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178 (Free link if you don't have Medium membership): https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178?sk=1cbf33a5d1fd2ed841f9487972c1cbed Thumbnail photo by Alexander London on Unsplash https://unsplash.com/@alxndr_london	People & Blogs	64	2
8o3jvkK2GGU	UCv83tO5cePwHMt1952IVVHw	Python Environment Setup for Machine Learning	2020-12-23 13:50:07 UTC	2020-12-23 13:53:02 UTC	754 seconds	Everything you need for a Python environment set up for Machine Learning and Data Science! 📕 Article: https://towardsdatascience.com/how-to-setup-python-for-machine-learning-173cb25f0206 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Thumbnail background by Christian Wiediger on Unsplash https://unsplash.com/@christianw	People & Blogs	38	1
BYbJ_HH788U	UCv83tO5cePwHMt1952IVVHw	Functional API - TensorFlow Essentials #2	2020-12-28 16:41:11 UTC	2020-12-29 10:04:40 UTC	341 seconds	A look at the functional API method for building models in TensorFlow 2 for Python. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Thumbnail background by Darius Bashar on Unsplash https://unsplash.com/@dariusbashar?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText	Education	20	0
_8Bydxud1XU	UCv83tO5cePwHMt1952IVVHw	Training Parameters - TensorFlow Essentials #3	2020-12-28 19:30:23 UTC	2020-12-29 23:37:57 UTC	450 seconds	Learn how to set up model training parameters and compile the model before training. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Thumbnail background by Alex McCarthy on Unsplash https://unsplash.com/@4lexmccarthy?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText	Education	17	0
f6XVfgJTbp4	UCv83tO5cePwHMt1952IVVHw	Input Data Pipelines - TensorFlow Essentials #4	2020-12-28 23:25:54 UTC	2020-12-30 11:30:02 UTC	751 seconds	Learn how to set-up efficient and clean input data pipelines using tf.data.Dataset 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Thumbnail background by Daria Nepriakhina on Unsplash https://unsplash.com/@epicantus?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText	Education	54	0
MQD1yMnZ_jk	UCv83tO5cePwHMt1952IVVHw	Sequential Model - TensorFlow Essentials #1	2020-12-29 09:46:00 UTC	2020-12-29 09:50:23 UTC	391 seconds	Learn how to use the sequential model building approach in TensorFlow 2. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Background thumbnail by Aryan Dhiman on Unsplash https://unsplash.com/@mylifeasaryan_?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText	Education	84	1
KTFWNI0qL28	UCv83tO5cePwHMt1952IVVHw	6 of Python's Newest and Best Features (3.7-3.9)	2021-01-12 23:31:26 UTC	2021-01-12 23:58:12 UTC	1084 seconds	A rundown of the six most recent, and coolest features added to Python in the past few years! 2018 brought us a plethora of new features with the release of Python 3.7, followed by 3.8 in 2019, and 3.9 in 2020. Many of those changes were behind the scenes. Optimizations and upgrades that the vast majority of us will never notice, despite their benefits. Others are more obvious, additions to syntax or functionality that can change how we write our code. But even these visible changes can be hard to keep up with. In this video, we will run through the more apparent upgrades to provide a brief but hopefully invaluable refresher on everything new to Python from the past few years. - Python 3.7 - Breakpoints - Python 3.8 - Walrus Operator - F-string '=' Specifier - Positional-only Parameters - Python 3.9 - More Type Hinting - Dictionary Unions Medium Article: https://towardsdatascience.com/amazing-features-added-to-python-from-3-7-to-now-4f35f0bb1ea6 (Free access link): https://towardsdatascience.com/amazing-features-added-to-python-from-3-7-to-now-4f35f0bb1ea6?sk=bda3cb7717caa969b81619f85191f241 Thumbnail background by Martin Sanchez on Unsplash: https://unsplash.com/photos/4PDPLw1flgE 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	Education	15	2
GyJtxd14DTc	UCv83tO5cePwHMt1952IVVHw	Novice to Advanced RegEx in Less-than 30 Minutes + Python	2021-01-27 09:06:42 UTC	2021-01-27 09:51:32 UTC	1769 seconds	A full tutorial covering everything you need to know about Regular Expressions - an essential for anyone learning to code - and even more so for anyone interested in Natural Language Processing. This video includes: - metacharacters - quantifiers - capture groups - using capture groups in Python - character sets - look-ahead and look-behind assertions - negative look-ahead and look-behind assertions - inline modifiers - passing modifiers as function parameters in Python - conditionals (if-else statements for RegEx) - re.match - re.search - re.findall We cover all of this in-depth in this tutorial, incl. examples all the way through on RegEx101 (an interactive debugging/regex building tool) and also in Python. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	Education	239	8
1ZcXmjZtJJ8	UCv83tO5cePwHMt1952IVVHw	Building a PlotLy $GME Chart in Python	2021-02-02 13:38:16 UTC	2021-02-07 13:24:45 UTC	4492 seconds	A code-along video covering the coding process from imagination to Python. Something a little different, I'm not overly keen on this format - it's pretty long - but I've recorded it and I think maybe this can be useful for a few of you. I haven't prepared anything beforehand, this is just going into the coding process with a rough outline of wanting to build a stock chart for GME (GameStop) and adding a few technical indicators - to get more familiar with PlotLy and the AlphaVantage API. So, it's a weird one, but I hope a few of you enjoy it - thanks :) 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	Education	10	0
ZIRmXkHp0-c	UCv83tO5cePwHMt1952IVVHw	How to Build Custom Q&A Transformer Models in Python	2021-02-09 20:42:56 UTC	2021-02-12 13:30:03 UTC	4216 seconds	In this video, we will learn how to take a pre-trained transformer model and train it for question-and-answering. We will be using the HuggingFace transformers library with the PyTorch implementation of models in Python. Transformers are one of the biggest developments in Natural Language Processing (NLP) and learning how to use them properly is basically a data science superpower - they're genuinely amazing I promise! I hope you enjoy the video :) 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/the-ultimate-performance-metric-in-nlp-111df6c64460 (Free link): https://towardsdatascience.com/how-to-fine-tune-a-q-a-transformer-86f91ec92997?sk=9344fd51afe71a0905db833d0183d436 Code: https://gist.github.com/jamescalam/55daf50c8da9eb3a7c18de058bc139a3 Photo in thumbnail by Lorenzo Herrera on Unsplash https://unsplash.com/@lorenzoherrera	Education	163	5
FdjVoOf9HN4	UCv83tO5cePwHMt1952IVVHw	How-to Use The Reddit API in Python	2021-02-12 11:36:48 UTC	2021-02-12 12:02:48 UTC	1401 seconds	Learn how to use the Reddit API in Python, including setup, authorization, and pulling data from subreddits. Reddit API docs: https://www.reddit.com/dev/api/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/how-to-use-the-reddit-api-in-python-5e05ddfd1e5c 📖 Free link: https://towardsdatascience.com/how-to-use-the-reddit-api-in-python-5e05ddfd1e5c?sk=0295f297c1365bee7cc7a32bdff21b61 Extract from article: "Reddit is a huge ecosystem brimming with data that is readily available at our very fingertips. As a data-minded person, I wanted to take advantage of this and perform some analysis using this vast repository of open-source data. Initially, it turned out that getting to grip with Reddit’s API wasn’t as clear-cut as expected — despite being a straightforward process; it can be a little confusing at first. So, after figuring everything out, I wrote this article — which I hope will help a few of you to get familiar with using the Reddit API in Python. We will cover: Getting Access Making Requests - Reading the Data - Streaming New Posts Parameters Getting Access First, we need access. Unlike most popular services, the Reddit API was somewhat difficult to figure out initially. There are several steps: 1. Go to App Preferences and click create another app… at the bottom. 2. Fill out the required details, make sure to select script — and click create app. 3. make a note of the personal use script and secret tokens. 4. Request a temporary OAuth token from Reddit. We need our username and password for this. 5. Add headers=headers to every request. The OAuth token will expire after ~2 hours, and a new one will need to be requested. " And so on, check it out if you're interested in reading (rather than watching). 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	627	11
scJsty_DR3o	UCv83tO5cePwHMt1952IVVHw	How to Build Q&A Models in Python (Transformers)	2021-02-17 21:03:29 UTC	2021-02-19 15:00:21 UTC	1189 seconds	In this video we'll cover how to build a question-answering model in Python using HuggingFace's Transformers. You will need to install the transformers library with: pip install transformers Alongside either TensorFlow or PyTorch (to follow this video exactly you will need PyTorch). To install TensorFlow just type: pip install tensorflow OR conda install tensorflow And for PyTorch follow the instructions under 'Install PyTorch' here: https://pytorch.org/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Link to Q&A fine-tuning video: https://youtu.be/ZIRmXkHp0-c You can find the Medium article link below here: https://towardsdatascience.com/question-and-answering-with-bert-6ef89a78dac	Education	151	1
QJq9RTp_OVE	UCv83tO5cePwHMt1952IVVHw	How-to Decode Outputs From NLP Models (Python)	2021-02-21 18:02:42 UTC	2021-02-24 15:00:10 UTC	577 seconds	In this video, we will cover three ways to decode the output probabilities from NLP models - greedy search, random sampling, and beam search. Learning how to decode outputs can make a huge difference in diagnosing model issues and improving text output quality - and as an added bonus it's super easy. One of the often-overlooked parts of sequence generation in natural language processing (NLP) is how we select our output tokens — otherwise known as decoding. You may be thinking — we select a token/word/character based on the probability of each token assigned by our model. This is half-true — in language-based tasks, we typically build a model which outputs a set of probabilities to an array where each value in that array represents the probability of a specific word/token. At this point, it might seem logical to select the token with the highest probability? Well, not really — this can create some unforeseen consequences — as we will see soon. When we are selecting a token in machine-generated text, we have a few alternative methods for performing this decode — and options for modifying the exact behavior too. In this video we will explore three different methods for selecting our output token, these are: - Greedy Decoding - Random Sampling - Beam Search 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Link to the article version on Medium: https://towardsdatascience.com/the-three-decoding-methods-for-nlp-23ca59cb1e9d Free link (if you don't have membership): https://towardsdatascience.com/the-three-decoding-methods-for-nlp-23ca59cb1e9d?sk=64fbb0204c174dc520af027a69f88030	Education	28	0
TCZgXFPNnbc	UCv83tO5cePwHMt1952IVVHw	Identify Stocks on Reddit with SpaCy (NER in Python)	2021-03-01 21:47:29 UTC	2021-03-03 14:27:48 UTC	1307 seconds	We will learn how to process unstructured text data from Reddit and extract organization names so that any further analysis is automatically classified and results assigned to the correct stocks. Organizations are mentioned in each subreddit in a variety of formats. Typically we will find two formats: - Organization name, eg Tesla/Tesla Motors - Ticker symbol, eg TSLA, tsla, or $TSLA We also need to be able to differentiate between tickers and other abbreviations/slang -some of these are unclear like AI (AI can mean both artificial intelligence and refer to the ticker symbol for C3.ai). So, we need a reasonable competent NER process to accurately classify our data. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Reddit API video: https://youtu.be/FdjVoOf9HN4 /r/investing data: https://github.com/jamescalam/transformers/blob/main/course/named_entity_recognition/data/reddit_investing.csv Medium article: https://towardsdatascience.com/ner-for-extracting-stock-mentions-on-reddit-aa604e577be (Free version if you don't have Medium membership): https://towardsdatascience.com/ner-for-extracting-stock-mentions-on-reddit-aa604e577be?sk=d16305d40b18e7955a0665633182d2b4 Thanks for watching!	Education	33	0
yDGo9z_RlnE	UCv83tO5cePwHMt1952IVVHw	Sentiment Analysis on ANY Length of Text With Transformers (Python)	2021-03-10 08:15:21 UTC	2021-03-10 13:15:03 UTC	1630 seconds	The de-facto standard in many natural language processing (NLP) tasks nowadays is to use a transformer. Text generation? Transformer. Question-and-answering? Transformer. Language classification? Transformer! However, one of the problems with many of these models (a problem that is not just restricted to transformer models) is that we cannot process long pieces of text. Almost every article I write on Medium contains 1000+ words, which, when tokenized for a transformer model like BERT, will produce 1000+ tokens. BERT (and many other transformer models) will consume 512 tokens max - truncating anything beyond this length. Although I think you may struggle to find value in processing my Medium articles, the same applies to many useful data sources - like news articles or Reddit posts. We will take a look at how we can work around this limitation. In this article, we will find the sentiment for long posts from the /r/investing subreddit. This video will cover: High-Level Approach Getting Started - Data - Initialization Tokenization Preparing The Chunks - Split - CLS and SEP - Padding - Reshaping For BERT Making Predictions 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Here's a link to the Medium article: https://towardsdatascience.com/how-to-apply-transformers-to-any-length-of-text-a5601410af7f And a free access link if you don't have Medium membership: https://towardsdatascience.com/how-to-apply-transformers-to-any-length-of-text-a5601410af7f?sk=d4e717eb2ff31fb27ea67019bbb63ad6	Education	111	2
9Od9-DV9kd8	UCv83tO5cePwHMt1952IVVHw	Unicode Normalization for NLP in Python	2021-03-16 09:27:24 UTC	2021-03-17 13:30:00 UTC	927 seconds	ℕ𝕠-𝕠𝕟𝕖 𝕚𝕟 𝕥𝕙𝕖𝕚𝕣 𝕣𝕚𝕘𝕙𝕥 𝕞𝕚𝕟𝕕 𝕨𝕠𝕦𝕝𝕕 𝕖𝕧𝕖𝕣 𝕦𝕤𝕖 𝕥𝕙𝕖𝕤𝕖 𝕒𝕟𝕟𝕠𝕪𝕚𝕟𝕘 𝕗𝕠𝕟𝕥 𝕧𝕒𝕣𝕚𝕒𝕟𝕥𝕤. 𝕋𝕙𝕖 𝕨𝕠𝕣𝕤𝕥 𝕥𝕙𝕚𝕟𝕘, 𝕚𝕤 𝕚𝕗 𝕪𝕠𝕦 𝕕𝕠 𝕒𝕟𝕪 𝕗𝕠𝕣𝕞 𝕠𝕗 ℕ𝕃ℙ 𝕒𝕟𝕕 𝕪𝕠𝕦 𝕙𝕒𝕧𝕖 𝕔𝕙𝕒𝕣𝕒𝕔𝕥𝕖𝕣𝕤 𝕝𝕚𝕜𝕖 𝕥𝕙𝕚𝕤 𝕚𝕟 𝕪𝕠𝕦𝕣 𝕚𝕟𝕡𝕦𝕥, 𝕪𝕠𝕦𝕣 𝕥𝕖𝕩𝕥 𝕓𝕖𝕔𝕠𝕞𝕖𝕤 𝕔𝕠𝕞𝕡𝕝𝕖𝕥𝕖𝕝𝕪 𝕦𝕟𝕣𝕖𝕒𝕕𝕒𝕓𝕝𝕖. We also find that text like this is incredibly common - particularly on social media. Another pain-point comes from diacritics (the little glyphs in Ç, é, Å) that you'll find in almost every European language. These characters have a hidden property that can trip up any NLP model - take a look at the Unicode for two versions of Ç: Latin capital letter C with cedilla: \u00C7 Latin capital letter C + combining cedilla: \u0043\u0327 Both are completely different, despite rendering as the same character. To deal with all of these text variants we need to use Unicode normalization - which we will cover in this video. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/what-on-earth-is-unicode-normalization-56c005c55ad0 Friend link (free access): https://towardsdatascience.com/what-on-earth-is-unicode-normalization-56c005c55ad0?sk=0cd19a9ad9f5d948b33179bab3c3b7cd	Education	43	0
2qJavL-VX9Y	UCv83tO5cePwHMt1952IVVHw	The NEW Match-Case Statement in Python 3.10	2021-03-17 20:37:52 UTC	2021-03-19 16:00:03 UTC	1088 seconds	Python 3.10 is beginning to fill-out with plenty of fascinating new features. One of those, in particular, caught my attention - structural pattern matching - or as most of us will know it, switch/case statements. Switch-statements have been absent from Python despite being a common feature of most languages. Python is leapfrogging ahead of those languages by introducing the match-case statement as a switch-case v2.0. Back in 2006, PEP 3103 was raised, recommending the implementation of a switch-case statement. However, after a poll at PyCon 2007 received no support for the feature, the Python devs dropped it. Fast-forward to 2020, and Guido van Rossum, the creator of Python, committed the first documentation showing the new match-statements, which have been named Structural Pattern Matching, as found in PEP 634. Let's take a look at how this new logic works. Medium Article: https://towardsdatascience.com/switch-case-statements-are-coming-to-python-d0caf7b2bfd3 Friend Link (free access): https://towardsdatascience.com/switch-case-statements-are-coming-to-python-d0caf7b2bfd3?sk=363e0f7696502647e007f91910b4c817 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 00:58 Switch-Case 02:37 Flow of Logic 03:21 Second Example (Tuples) 05:00 Final Example Setup 11:30 Final Example If-Else Version 15:22 Final Example Match-Case Version	Education	310	11
pjtnkCGElcE	UCv83tO5cePwHMt1952IVVHw	Multi-Class Language Classification With BERT in TensorFlow	2021-03-24 17:51:01 UTC	2021-03-25 16:00:15 UTC	2604 seconds	Chapters for each section of the video (preprocessing, model build, prediction) are in the video timeline. Transformers have been described as the fourth pillar of deep learning [1], alongside the three big neural net architectures of CNNs, RNNs, and MLPs. However, from the perspective of natural language processing - transformers are much more than that. Since their introduction in 2017, they've come to dominate a majority of NLP benchmarks - and continue to impress daily. What I'm saying is, transformers are damn cool. And with libraries like HuggingFace's transformers - it has become too easy to build incredible solutions with them. So, what's not to love? Incredible performance paired with the ultimate ease-of-use. In this video, we'll work through building a multi-class classification model using transformers - from start-to-finish. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/multi-class-classification-with-transformers-6cf7b59a033a Free access: https://towardsdatascience.com/multi-class-classification-with-transformers-6cf7b59a033a?sk=544872025c2283c54cf4294814b8cae3 Link to Kaggle video: https://youtu.be/DgGFhQmfxHo [1] Fourth Pillar of AI: https://ark-invest.com/articles/analyst-research/transformers-comprise-the-fourth-pillar-of-deep-learning/ 00:00 Intro 01:21 Pulling Data 01:47 Preprocessing 14:33 Data Input Pipeline 24:14 Defining Model 33:29 Model Training 35:36 Saving and Loading Models 37:37 Making Predictions	Education	264	1
JkeNVaiUq_c	UCv83tO5cePwHMt1952IVVHw	How to Build Python Packages for Pip	2021-04-02 14:51:14 UTC	2021-04-02 15:19:32 UTC	1267 seconds	The most powerful feature of Python is its community. Almost every use-case out there has a package built specifically for it. Need to send mobile/email alerts? pip install knockknock - Build ML apps? pip install streamlit - Bored of your terminal? pip install colorama - It's too easy! I know this is obvious, but those libraries didn't magically appear. For each package, there is a person, or many persons - that actively developed and deployed that package. Every single one. All 300K+ of them. That is why Python is Python, the level of support is phenomenal - mindblowing. In this video, we will learn how to build our own packages. And add them to the Python Package Index (PyPI). Afterward, we will be able to install our packages using pip install! GitHub Repo: https://github.com/jamescalam/aesthetic_ascii 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium Article: https://towardsdatascience.com/how-to-package-your-python-code-df5a7739ab2e 📖 Here's a free link: https://towardsdatascience.com/how-to-package-your-python-code-df5a7739ab2e?sk=04d9f67c0654445bbcbbf6825f535900	Education	390	11
4Jmq28RQ3hU	UCv83tO5cePwHMt1952IVVHw	How-to Structure a Q&A ML App	2021-04-09 15:02:44 UTC	2021-04-09 15:22:50 UTC	585 seconds	▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB I'm planning on doing something different, a series of videos where we work through the steps - from start-to-finish - of (attempting) to build a Q&A web app that answers our questions with Stoic answers. In this video, I'm outlining the idea and describing the high-level setup that I think we'll need to put together. It should be cool! We'll be using the Haystack framework for 'Q&A at scale', which using HuggingFace transformers under-the-hood, and the Elasticsearch document store. Find the repo here: https://github.com/jamescalam/aurelius 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	Education	46	0
Vwq7Ucp9UCw	UCv83tO5cePwHMt1952IVVHw	How to Index Q&A Data With Haystack and Elasticsearch	2021-04-11 21:30:32 UTC	2021-04-12 15:00:11 UTC	807 seconds	▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB The second video in 'Building a Stoic Q&A App' - here we're setting up Elasticsearch and Haystack to store the data (Meditations) ready for retrieval when we ask our app questions. Find the code here: https://github.com/jamescalam/aurelius/tree/main/code/labs 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	Education	79	3
DBsxUSUhfRg	UCv83tO5cePwHMt1952IVVHw	Q&A Document Retrieval With DPR	2021-04-12 14:44:59 UTC	2021-04-15 15:00:10 UTC	890 seconds	▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB The third video in building our Stoic Q&A app. In open-domain question answering, we typically design a model architecture that contains a data source, retriever, and reader/generator. The first of these components is typically a document store. The two most popular stores we use here are Elasticsearch and FAISS. Next up is our retriever — the topic of this video. The job of the retriever is to filter through our document store for relevant chunks of information (the documents) and pass them to the reader/generator model. DPR (dense passage retriever) is a dense vector retriever that is trained on question-context pairs. Encoding both accordingly - enabling super accurate similarity indexing. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 If you're interested in learning more about DPR, I wrote about it on Medium here: https://towardsdatascience.com/how-to-create-an-answer-from-a-question-with-dpr-d76e29cc5d60 (Free link): https://towardsdatascience.com/how-to-create-an-answer-from-a-question-with-dpr-d76e29cc5d60?sk=1bdd7c1bff80bf51410962691c690c69 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	57	0
QrzHImDEq_w	UCv83tO5cePwHMt1952IVVHw	How to Use Type Annotations in Python	2021-04-23 21:44:38 UTC	2021-04-27 14:53:25 UTC	907 seconds	Type annotations - also known as type signatures - are used to indicate the datatypes of variables and input/outputs of functions and methods. In many languages, datatypes are explicitly stated. In these languages, if you don't declare your datatype - the code will not run. Type annotations have a long and convoluted history with Python, going all the way back to the first release of Python 3 with the initial implementation of function annotations. Type annotations in Python are not make-or-break like in other languages (like C). They're optional chunks of syntax that we can add to make our code more explicit. Erroneous type annotations will do nothing more than highlight the incorrect annotation in our code editor - no errors are ever raised due to annotations. So, if type annotations are not enforced, why use them? Well, as we touched upon already - declaring types makes our code more explicit, and if done well, easier to read - both for ourselves and others. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Read the Medium article here: https://towardsdatascience.com/type-annotations-in-python-d90990b172dc 📖 Here's a free link: https://towardsdatascience.com/type-annotations-in-python-d90990b172dc?sk=29bc29ab5478a842363963b421781b47 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 00:55 Datatypes Example in C 2:53 Static and Dynamic Typed Languages 3:47 Type Annotations in Python 4:25 How to Define Simple Types 6:04 IDE Warnings 8:20 More Complex Types 9:53 dict[str, int] 11.07 Multiple Types 11:38 Union Operator (Py 3.9) 12:34 Union Operator (Py 3.10) 13:21 Optional Operator	Education	132	3
2tdLYIKPafc	UCv83tO5cePwHMt1952IVVHw	Extractive Q&A With Haystack and FastAPI in Python	2021-04-26 22:03:55 UTC	2021-04-29 15:00:04 UTC	1058 seconds	▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB In this video we work through building an extractive Q&A stack using Haystack, and embedding it within a FastAPI instance in Python. We use the BERT transformer for our reader model, alongside Elasticsearch and the BM25 retriever algorithm. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	71	1
jVPd7lEvjtg	UCv83tO5cePwHMt1952IVVHw	Sentence Similarity With Transformers and PyTorch (Python)	2021-05-04 15:25:17 UTC	2021-05-05 15:00:20 UTC	1270 seconds	Easy mode: https://youtu.be/Ey81KfQ3PQU All we ever seem to talk about nowadays are BERT this, BERT that. I want to talk about something else, but BERT is just too good - so this video will be about BERT for sentence similarity. A big part of NLP relies on similarity in highly-dimensional spaces. Typically an NLP solution will take some text, process it to create a big vector/array representing said text - then perform several transformations. It's highly-dimensional magic. Sentence similarity is one of the clearest examples of how powerful highly-dimensional magic can be. The logic is this: - Take a sentence, convert it into a vector. - Take many other sentences, and convert them into vectors. - Find sentences that have the smallest distance (Euclidean) or smallest angle (cosine similarity) between them - more on that here. - We now have a measure of semantic similarity between sentences - easy! At a high level, there's not much else to it. But of course, we want to understand what is happening in a little more detail and implement this in Python too. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/bert-for-measuring-text-similarity-eec91c6bf9e1 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/bert-for-measuring-text-similarity-eec91c6bf9e1?sk=c0f2990b4660210b447e52d55bd0f4e5 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 00:16 BERT Base Network 1:11 Sentence Vectors and Similarity 1:47 The Data and Model 3:01 Two Approaches 3:16 Tokenizing Sentences 9:11 Creating last_hidden_state Tensor 11:08 Creating Sentence Vectors 17:53 Cosine Similarity	Education	233	2
Ey81KfQ3PQU	UCv83tO5cePwHMt1952IVVHw	Sentence Similarity With Sentence-Transformers in Python	2021-05-04 19:55:42 UTC	2021-05-05 15:00:09 UTC	370 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp Hard mode: https://youtu.be/jVPd7lEvjtg All we ever seem to talk about nowadays are BERT this, BERT that. I want to talk about something else, but BERT is just too good - so this video will be about BERT for sentence similarity. A big part of NLP relies on similarity in highly-dimensional spaces. Typically an NLP solution will take some text, process it to create a big vector/array representing said text - then perform several transformations. It's highly-dimensional magic. Sentence similarity is one of the clearest examples of how powerful highly-dimensional magic can be. The logic is this: - Take a sentence, convert it into a vector. - Take many other sentences, and convert them into vectors. - Find sentences that have the smallest distance (Euclidean) or smallest angle (cosine similarity) between them - more on that here. - We now have a measure of semantic similarity between sentences - easy! At a high level, there's not much else to it. But of course, we want to understand what is happening in a little more detail and implement this in Python too. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/bert-for-measuring-text-similarity-eec91c6bf9e1 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/bert-for-measuring-text-similarity-eec91c6bf9e1?sk=c0f2990b4660210b447e52d55bd0f4e5 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	370	4
W8ZPQOcHnlE	UCv83tO5cePwHMt1952IVVHw	NER With Transformers and spaCy (Python)	2021-05-09 20:57:10 UTC	2021-05-11 15:00:28 UTC	567 seconds	Named entity recognition (NER) consists of extracting 'entities' from text - what we mean by that is given the sentence: "Apple reached an all-time high stock price of 143 dollars this January." We might want to extract the key pieces of information - or 'entities' - and categorize each of those entities. Like so: - Apple : Organization - 143 dollars : Monetary Value - this January : Date For us humans, this is easy. But how can we teach a machine to distinguish between a granny smith apple and the Apple we trade on NASDAQ? (No, we can't rely on the 'A' being capitalized…) This is where NER comes in - using NER, we can extract keywords like apple and identify that it is, in fact, an organization - not a fruit. The go-to library for NER is spaCy, which is incredible. But what if we added transformers to spaCy? Even better - we'll cover exactly that in this video. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5	Education	120	2
q9NS5WpfkrU	UCv83tO5cePwHMt1952IVVHw	Training BERT #1 - Masked-Language Modeling (MLM)	2021-05-19 09:31:26 UTC	2021-05-19 14:51:39 UTC	984 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp BERT, everyone's favorite transformer costs Google ~$7K to train (and who knows how much in R&D costs). From there, we write a couple of lines of code to use the same model - all for free. BERT has enjoyed unparalleled success in NLP thanks to two unique training approaches, masked-language modeling (MLM), and next sentence prediction (NSP). MLM consists of giving BERT a sentence and optimizing the weights inside BERT to output the same sentence on the other side. So we input a sentence and ask that BERT outputs the same sentence. However, before we actually give BERT that input sentence - we mask a few tokens. So we're actually inputting an incomplete sentence and asking BERT to complete it for us. How to train BERT with MLM: https://youtu.be/R6hcxMMOrPE 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Medium article: https://towardsdatascience.com/masked-language-modelling-with-bert-7d49793e5d2c 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/masked-language-modelling-with-bert-7d49793e5d2c?sk=17a19eca8dc8280bea4138802580ffe0 🤖 70% Discount on the NLP With Transformers in Python course: https://www.udemy.com/course/nlp-with-transformers/?couponCode=MEDIUM3 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	277	3
R6hcxMMOrPE	UCv83tO5cePwHMt1952IVVHw	Training BERT #2 - Train With Masked-Language Modeling (MLM)	2021-05-19 11:38:10 UTC	2021-05-19 14:51:49 UTC	1666 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp BERT has enjoyed unparalleled success in NLP thanks to two unique training approaches, masked-language modeling (MLM), and next sentence prediction (NSP). In many cases, we might be able to take the pre-trained BERT model out-of-the-box and apply it successfully to our own language tasks. But often, we might need to pre-train the model for a specific use case even further. Further training with MLM allows us to tune BERT to better understand the particular use of language in a more specific domain. Out-of-the-box BERT - great for general purpose use. Fine-tuned with MLM BERT - great for domain-specific use. In this video, we'll cover exactly how to fine-tune BERT models using MLM in PyTorch. 👾 Code: https://github.com/jamescalam/transformers/blob/main/course/training/03_mlm_training.ipynb Meditations data: https://github.com/jamescalam/transformers/blob/main/data/text/meditations/clean.txt Understanding MLM: https://youtu.be/q9NS5WpfkrU 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/masked-language-modelling-with-bert-7d49793e5d2c 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/masked-language-modelling-with-bert-7d49793e5d2c?sk=17a19eca8dc8280bea4138802580ffe0 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	223	1
1gN1snKBLP0	UCv83tO5cePwHMt1952IVVHw	Training BERT #3 - Next Sentence Prediction (NSP)	2021-05-23 18:14:04 UTC	2021-05-25 14:56:47 UTC	823 seconds	Next sentence prediction (NSP) is one-half of the training process behind the BERT model (the other being masked-language modeling - MLM). Where MLM teaches BERT to understand relationships between words - NSP teaches BERT to understand relationships between sentences. In the original BERT paper, it was found that without NSP, BERT performed worse on every single metric - so it's important. Now, when we use a pre-trained BERT model, training with NSP and MLM has already been done, so why do we need to know about it? Well, we can actually further pre-train these pre-trained BERT models so that they better understand the language used in our specific use-cases. To do that, we can use both MLM and NSP. So, in this video, we'll go into depth on what NSP is, how it works, and how we can implement it in code. Training with NSP: https://youtu.be/x1lAcT3xl5M 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/bert-for-next-sentence-prediction-466b67f8226f 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/bert-for-next-sentence-prediction-466b67f8226f?sk=3595968413abde1c5833e1a96e449673 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	94	6
x1lAcT3xl5M	UCv83tO5cePwHMt1952IVVHw	Training BERT #4 - Train With Next Sentence Prediction (NSP)	2021-05-27 15:52:57 UTC	2021-05-27 16:15:39 UTC	2205 seconds	Next sentence prediction (NSP) is one-half of the training process behind the BERT model (the other being masked-language modeling - MLM). Although NSP (and MLM) are used to pre-train BERT models, we can use these exact methods to further pre-train our models to better understand the specific style of language in our own use cases. So, in this video, we'll cover exactly how we take an unstructured body of text, and use it to pre-train a BERT model using NSP. Meditations data: https://github.com/jamescalam/transformers/blob/main/data/text/meditations/clean.txt Jupyter Notebook https://github.com/jamescalam/transformers/blob/main/course/training/06_nsp_training.ipynb 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/bert-for-next-sentence-prediction-466b67f8226f 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/bert-for-next-sentence-prediction-466b67f8226f?sk=3595968413abde1c5833e1a96e449673 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	95	1
5-A435hIYio	UCv83tO5cePwHMt1952IVVHw	New Features in Python 3.10	2021-06-03 16:41:56 UTC	2021-06-08 15:00:02 UTC	800 seconds	The Python 3.10 release has several new features like structural pattern matching, a new typing Union operator, and parenthesized context managers! Python 3.10 has now been released, here we test all of the best new features introduced. We'll cover some of the most interesting additions to Python - structural pattern matching, parenthesized context managers, more typing, and the new and improved error messages. Download the latest release: https://www.python.org/downloads/release/python-3100/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/whats-new-in-python-3-10-a757c6c69342 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/whats-new-in-python-3-10-a757c6c69342?sk=648ae12c1025a83affba4eecec0d46c6 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 00:45 Type Annotations in Python 01:10 Typing Union Operator 02:07 Parenthesized Context Managers 05:07 Structural Pattern Matching 09:31 Better Error Messages	Education	375	2
IC9FaVPKlYc	UCv83tO5cePwHMt1952IVVHw	Training BERT #5 - Training With BertForPretraining	2021-06-04 05:13:06 UTC	2021-06-15 15:00:19 UTC	1306 seconds	NSP Logic https://youtu.be/1gN1snKBLP0 MLM Logic https://youtu.be/q9NS5WpfkrU 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/how-to-train-bert-aaad00533168 📖 Here's a free link: https://towardsdatascience.com/how-to-train-bert-aaad00533168?sk=5ad4e5e44a6c573b3be1967c9abdcc35 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	128	1
fA0dFQacmic	UCv83tO5cePwHMt1952IVVHw	FREE 11 Hour NLP Transformers Course (Next 3 Days Only)	2021-06-04 07:56:44 UTC	2021-06-04 13:00:19 UTC	267 seconds	The offer has now expired! You can find the final 70% discount here: https://bit.ly/3DFvvY5 In total, 10823 people redeemed the code - which is incredible, I'm very happy so many of you were interested in the course and I hope it will help many of you in learning about transformers and NLP where it may have been too expensive to otherwise - so thank you all! 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	51	0
GhGUZrcB-WM	UCv83tO5cePwHMt1952IVVHw	How-to Use HuggingFace's Datasets - Transformers From Scratch #1	2021-06-21 21:56:31 UTC	2021-06-22 13:00:07 UTC	861 seconds	How can we build our own custom transformer models? Maybe we'd like our model to understand a less common language, how many transformer models out there have been trained on Piemontese or the Nahuatl languages? In that case, we need to do something different. We need to build our own model - from scratch. In this video, we'll learn how to use HuggingFace's datasets library to download multilingual data and prepare it for training our custom transformer tokenizer and model. --- Part 2: https://youtu.be/JIeAB8vvBQo Part 3: https://youtu.be/heTYbpr9mD8 Part 4: https://youtu.be/35Pdoyi6ZoQ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/transformers-from-scratch-creating-a-tokenizer-7d7418adb403 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/transformers-from-scratch-creating-a-tokenizer-7d7418adb403?sk=aea909609f41be43bdb2dbbd75a801f2 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	147	3
JIeAB8vvBQo	UCv83tO5cePwHMt1952IVVHw	Build a Custom Transformer Tokenizer - Transformers From Scratch #2	2021-06-22 20:07:37 UTC	2021-06-24 14:00:06 UTC	857 seconds	How can we build our own custom transformer models? Maybe we'd like our model to understand a less common language, how many transformer models out there have been trained on Piemontese or the Nahuatl languages? In that case, we need to do something different. We need to build our own model - from scratch. In this video, we'll learn how to use HuggingFace's tokenizers library to build our own custom transformer tokenizer. Part 1: https://youtu.be/GhGUZrcB-WM --- Part 3: https://youtu.be/heTYbpr9mD8 Part 4: https://youtu.be/35Pdoyi6ZoQ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 📙 Medium article: https://towardsdatascience.com/transformers-from-scratch-creating-a-tokenizer-7d7418adb403 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/transformers-from-scratch-creating-a-tokenizer-7d7418adb403?sk=aea909609f41be43bdb2dbbd75a801f2 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	80	3
ziiF1eFM3_4	UCv83tO5cePwHMt1952IVVHw	3 Vector-based Methods for Similarity Search (TF-IDF, BM25, SBERT)	2021-06-28 13:25:28 UTC	2021-06-29 13:00:23 UTC	1764 seconds	Vector similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together. Similarity search is a complex topic and there are countless techniques for building effective search engines. In this video, we'll cover three vector-based approaches for comparing languages and identifying similar 'documents', covering both vector similarity search and semantic search: - TF-IDF - BM25 - Sentence-BERT 📰 Original article: https://www.pinecone.io/learn/semantic-search/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership Mining Massive Datasets Book (Similarity Search): 📚 https://amzn.to/3CC0zrc (3rd ed) 📚 https://amzn.to/3AtHSnV (1st ed, cheaper) 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 01:37 TF-IDF 11:44 BM25 20:30 SBERT	Education	415	1
AY62z7HrghY	UCv83tO5cePwHMt1952IVVHw	3 Traditional Methods for Similarity Search (Jaccard, w-shingling, Levenshtein)	2021-06-28 17:44:01 UTC	2021-06-29 12:00:04 UTC	1520 seconds	Similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together. Similarity search is a complex topic and there are countless techniques for building effective search engines. In this video, we'll cover three traditional approaches for comparing languages and identifying similar 'documents': - Jaccard Similarity - w-shingling - Levenshtein distance 📰 Original article: https://www.pinecone.io/learn/semantic-search/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership Mining Massive Datasets Book (Similarity Search): 📚 https://amzn.to/3CC0zrc (3rd ed) 📚 https://amzn.to/3AtHSnV (1st ed, cheaper) 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 00:23 Jaccard Similarity 02:39 w-shingling 07:17 Levenshtein Distance	Education	86	0
heTYbpr9mD8	UCv83tO5cePwHMt1952IVVHw	Building MLM Training Input Pipeline - Transformers From Scratch #3	2021-07-02 15:28:46 UTC	2021-07-05 14:00:30 UTC	1392 seconds	The input pipeline of our training process is the more complex part of the entire transformer build. It consists of us taking our raw OSCAR training data, transforming it, and preparing it for Masked-Language Modeling (MLM). Finally, we load our data into a DataLoader ready for training! Part 1: https://youtu.be/GhGUZrcB-WM Part 2: https://youtu.be/JIeAB8vvBQo --- Part 4: https://youtu.be/35Pdoyi6ZoQ 📙 Medium article: https://towardsdatascience.com/how-to-train-a-bert-model-from-scratch-72cfce554fc6 📖 Free link: https://towardsdatascience.com/how-to-train-a-bert-model-from-scratch-72cfce554fc6?sk=9db6224efbd4ec6fd407a80b528e69b0 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Education	69	0
ee71R4Cqb5o	UCv83tO5cePwHMt1952IVVHw	Angular App Setup With Material - Stoic Q&A #5	2021-07-05 08:50:04 UTC	2021-07-20 14:00:28 UTC	814 seconds	▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB The fifth video in our Stoic Q&A series - setting up our Angular app with Angular Material. Prerequisites: Installation of Node.js and NPM - https://nodejs.org/en/ Angular - https://angular.io/guide/setup-local 👾 Discord https://discord.gg/c5QtDB9RAP	Science & Technology	17	0
35Pdoyi6ZoQ	UCv83tO5cePwHMt1952IVVHw	Training and Testing an Italian BERT - Transformers From Scratch #4	2021-07-05 18:22:41 UTC	2021-07-06 13:00:03 UTC	1838 seconds	We need two things for training, our DataLoader and a model. The DataLoader we have — but no model. For training, we need a raw (not pre-trained) RobertaForMaskedLM. To create that, we first need to create a RoBERTa config object to describe the parameters we’d like to initialize FiliBERTo with. Once we have our model, we set up our training loop and train! Post-training, we'll test the model with Laura, who is Italian - and hope for the best. Part 1: https://youtu.be/GhGUZrcB-WM Part 2: https://youtu.be/JIeAB8vvBQo Part 3: https://youtu.be/heTYbpr9mD8 --- 📙 Medium article: https://towardsdatascience.com/how-to-train-a-bert-model-from-scratch-72cfce554fc6 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/how-to-train-a-bert-model-from-scratch-72cfce554fc6?sk=9db6224efbd4ec6fd407a80b528e69b0 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 00:35 Review of Code 02:02 Config Object 06:28 Setup For Training 10:30 Training Loop 14:57 Dealing With CUDA Errors 16:17 Training Results 19:52 Loss 21:18 Fill-mask Pipeline For Testing 21:54 Testing With Laura	Science & Technology	94	1
sKyvsdEv6rk	UCv83tO5cePwHMt1952IVVHw	Faiss - Introduction to Similarity Search	2021-07-09 13:47:26 UTC	2021-07-13 15:00:19 UTC	1896 seconds	Full Similarity Search Playlist: https://www.youtube.com/watch?v=AY62z7HrghY&list=PLIUOU7oqGTLhlWpTz4NnuT3FekouIVlqc&index=1 Facebook AI Similarity Search (FAISS) is one of the most popular implementations of efficient similarity search, but what is it - and how can we use it? What is it that makes FAISS special? How do we make the best use of this incredible tool? Fortunately, it's a brilliantly simple process to get started with. And in this video, we'll explore some of the options FAISS provides, how they work, and - most importantly - how FAISS can make our semantic search faster. 🌲 Pinecone Article: https://www.pinecone.io/learn/faiss-tutorial/ 📊 Data: https://github.com/jamescalam/data/tree/main/sentence_embeddings_15K Notebook: https://gist.github.com/jamescalam/7117aa92235a7f52141ad0654795aa48 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord https://discord.gg/c5QtDB9RAP Mining Massive Datasets Book (Similarity Search): 📚 https://amzn.to/3CC0zrc (3rd ed) 📚 https://amzn.to/3AtHSnV (1st ed, cheaper) 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	354	5
bWLvGGJLzF8	UCv83tO5cePwHMt1952IVVHw	Why are there so many Tokenization methods in HF Transformers?	2021-07-27 07:12:07 UTC	2021-07-27 14:00:10 UTC	1080 seconds	HuggingFace's transformers library is the de-facto standard for NLP - used by practitioners worldwide, it's powerful, flexible, and easy to use. It achieves this through a fairly large (and complex) code-base, which has resulted in the question: "Why are there so many tokenization methods in HuggingFace transformers?" Tokenization is the process of encoding a string of text into transformer-readable token ID integers. In this video we cover five different methods for this - do these all produce the same output, or is there a difference between them? 📙 Medium article: https://towardsdatascience.com/why-are-there-so-many-tokenization-methods-for-transformers-a340e493b3a8 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 📖 If membership is too expensive - here's a free link: https://towardsdatascience.com/why-are-there-so-many-tokenization-methods-for-transformers-a340e493b3a8?sk=4a7e8c88d331aef9103e153b5b799ff5 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	51	0
B7wmo_NImgM	UCv83tO5cePwHMt1952IVVHw	Choosing Indexes for Similarity Search (Faiss in Python)	2021-08-09 14:33:47 UTC	2021-08-09 15:04:10 UTC	1893 seconds	Facebook AI Similarity Search (Faiss) is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles - with incredible accuracy in sub-second timescales for billion+ size datasets. The success in Faiss is due to many reasons. One of those, in particular, is its flexibility. Faiss recognizes that there is no 'one-size-fits-all' in similarity search. Instead, Faiss comes with a wide range of search indexes - which we can mix and match to our choosing. However, this great flexibility produces a question - how do we know which size fits our use case? Which index do we choose? Should we use multiple indexes, or is one enough? This video will explore the pros and cons of some of the most important indexes - Flat, LSH, HNSW, and IVF. We will learn how we decide which to use and the impact of parameters in each index to build some of the best indexes for semantic search. 🌲 Pinecone Article: https://www.pinecone.io/learn/vector-indexes/ 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership Download script for Sift1M dataset: https://gist.github.com/jamescalam/a09a16c17b677f2cf9c019114711f3bf Similarity Search Series: https://www.youtube.com/playlist?list=PLIUOU7oqGTLhlWpTz4NnuT3FekouIVlqc 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 👾 Discord https://discord.gg/c5QtDB9RAP Mining Massive Datasets Book (Similarity Search): 📚 https://amzn.to/3CC0zrc (3rd ed) 📚 https://amzn.to/3AtHSnV (1st ed, cheaper) 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	122	1
e_SBq3s20M8	UCv83tO5cePwHMt1952IVVHw	Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python)	2021-08-19 16:53:50 UTC	2021-08-20 16:00:16 UTC	1627 seconds	Locality sensitive hashing (LSH) is a widely popular technique used in approximate nearest neighbor (ANN) search. The solution to efficient similarity search is a profitable one - it is at the core of several billion (and even trillion) dollar companies. LSH consists of a variety of different methods. In this video, we'll be covering the traditional approach - which consists of multiple steps - shingling, MinHashing, and the final banded LSH function. 🌲 Pinecone article: https://www.pinecone.io/learn/locality-sensitive-hashing/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 01:21 Overview 05:58 Shingling 08:45 Vocab 09:27 One-hot Encoding 11:10 MinHash 15:51 Signature Info 18:08 LSH 22:20 Tuning LSH	Science & Technology	208	19
8bOrMqEdfiQ	UCv83tO5cePwHMt1952IVVHw	How LSH Random Projection works in search (+Python)	2021-08-24 05:09:11 UTC	2021-08-24 16:00:04 UTC	1148 seconds	Locality sensitive hashing (LSH) is a widely popular technique used in approximate similarity search. The solution to efficient similarity search is a profitable one - it is at the core of several billion (and even trillion) dollar companies. The problem with similarity search is scale. Many companies deal with millions-to-billions of data points every single day. Given a billion data points, is it feasible to compare all of them with every search? Further, many companies are not performing single searches - Google deals with more than 3.8 million searches every minute. Billions of data points combined with high-frequency searches are problematic - and we haven't considered the dimensionality nor the similarity function itself. Clearly, an exhaustive search across all data points is unrealistic for larger datasets. The solution to searching impossibly huge datasets? Approximate search. Rather than exhaustively comparing every pair, we approximate - restricting the search scope only to high probability matches. 🌲 Pinecone article: https://www.pinecone.io/learn/locality-sensitive-hashing-random-projection/ Download Sift1M: https://gist.github.com/jamescalam/a09a16c17b677f2cf9c019114711f3bf IndexLSH for Fast Similarity Search in Faiss: https://youtu.be/ZLfdQq_u7Eo 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	66	3
ZLfdQq_u7Eo	UCv83tO5cePwHMt1952IVVHw	IndexLSH for Fast Similarity Search in Faiss	2021-08-24 05:25:21 UTC	2021-08-24 16:00:12 UTC	1119 seconds	Faiss - or Facebook AI Similarity Search - is an open-source framework built for enabling similarity search. Faiss has many super-efficient implementations of different indexes that we can use in similarity search. That long list of indexes includes IndexLSH - an easy-to-use implementation of everything we have covered so far in LSH. 🌲 Pinecone article: https://www.pinecone.io/learn/locality-sensitive-hashing-random-projection/ Download Sift1M: https://gist.github.com/jamescalam/a09a16c17b677f2cf9c019114711f3bf How LSH Random Projection works in search (+Python): https://youtu.be/8bOrMqEdfiQ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	27	0
BMYBwbkbVec	UCv83tO5cePwHMt1952IVVHw	Faiss - Vector Compression with PQ and IVFPQ (in Python)	2021-08-30 14:35:01 UTC	2021-08-30 15:30:04 UTC	1161 seconds	So far we’ve worked through the logic behind a simple, readable implementation of product quantization (PQ) in Python for semantic search. Realistically we wouldn’t use this because it is not optimized and we already have excellent implementations elsewhere. Instead, we would use a library like Faiss (Facebook AI Similarity Search) — or a production-ready service like Pinecone. We’ll take a look at how we can build a PQ index in Faiss, and we’ll even take a look at combining PQ with an Inverted File (IVF) step to improve search speed. Before we start, we need to get data. We will be using the Sift1M dataset. It can be downloaded and opened using this script: https://gist.github.com/jamescalam/928a374b85daffa49a565f3dc18d059c#file-get_sift1m-ipynb 🌲 Pinecone article: https://www.pinecone.io/learn/product-quantization/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	36	1
t9mRf2S5vDI	UCv83tO5cePwHMt1952IVVHw	Product Quantization for Vector Similarity Search (+ Python)	2021-08-30 15:22:47 UTC	2021-08-30 15:37:46 UTC	1777 seconds	Vector similarity search can require huge amounts of memory. Indexes containing 1M dense vectors (a small dataset in today’s world) will often require several GBs of memory to store. When building recommendation systems or semantic search engines, this is not acceptable. The problem of excessive memory usage is exasperated by high-dimensional data, and with ever-increasing dataset sizes, this can very quickly become unmanageable. Product quantization (PQ) is a popular method for dramatically compressing high-dimensional vectors to use 97% less memory, and for making nearest-neighbor search speeds 5.5x faster in our tests. A composite IVF+PQ index speeds up the search by another 16.5x without affecting accuracy, for a whopping total speed increase of 92x compared to non-quantized indexes. 🌲 Pinecone article: https://www.pinecone.io/learn/product-quantization/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff	Science & Technology	116	2
GEhmmcx1lvM	UCv83tO5cePwHMt1952IVVHw	Composite Indexes and the Faiss Index Factory	2021-09-11 17:27:12 UTC	2021-09-24 12:53:58 UTC	1063 seconds	In the world of vector search, there are many indexing methods and vector processing techniques that allow us to prioritize between recall, latency, and memory usage. Using specific methods such as IVF, PQ, or HNSW, we can often return good results. But for best performance we will usually want to use composite indexes. We can view a composite index as a step-by-step process of vector transformations and one or more indexing methods. Allowing us to place multiple indexes and/or processing steps together to create our ‘ideal’ index. For example, we can use an inverted file (IVF) index to reduce the scope of our search (increasing search speed), and then add a compression technique such as product quantization (PQ) to keep larger indexes within a reasonable size limit. Where there is the ability to customize indexes, there is the risk of producing indexes with unnecessarily poor recall, latency, or memory usage. We must know how composite indexes work if we want to build robust and high-performance vector similarity search applications. It is essential to understand where different indexes or vector transformations can be used — and when they are not needed. Part 2: https://youtu.be/3Wqh4iUupbM 🌲 Pinecone article: https://www.pinecone.io/learn/composite-indexes/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://jamescalam.medium.com/subscribe (it's free!) https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:54 Composite Indexes 06:43 Faiss Index Factory 11:34 Why we use Index Factory 17:11 Outro	Science & Technology	21	0
3Wqh4iUupbM	UCv83tO5cePwHMt1952IVVHw	Best Indexes for Similarity Search in Faiss	2021-09-12 07:02:26 UTC	2021-09-24 12:54:07 UTC	1582 seconds	In the world of vector search, there are many indexing methods and vector processing techniques that allow us to prioritize between recall, latency, and memory usage. Using specific methods such as IVF, PQ, or HNSW, we can often return good results. But for best performance we will usually want to use composite indexes. We can view a composite index as a step-by-step process of vector transformations and one or more indexing methods. Allowing us to place multiple indexes and/or processing steps together to create our ‘ideal’ index. For example, we can use an inverted file (IVF) index to reduce the scope of our search (increasing search speed), and then add a compression technique such as product quantization (PQ) to keep larger indexes within a reasonable size limit. Where there is the ability to customize indexes, there is the risk of producing indexes with unnecessarily poor recall, latency, or memory usage. We must know how composite indexes work if we want to build robust and high-performance vector similarity search applications. It is essential to understand where different indexes or vector transformations can be used — and when they are not needed. Part 1: https://youtu.be/GEhmmcx1lvM 🌲 Pinecone article: https://www.pinecone.io/learn/composite-indexes/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://jamescalam.medium.com/subscribe (it's free!) https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:30 IVFADC 03:30 IVFADC in Faiss 07:29 Multi-D-ADC 09:17 Multi-D-ADC in Faiss 14:43 IVF-HNSW 21:39 IVF-HNSW in Faiss 25:58 Outro	Science & Technology	31	0
cR4qMSIvX28	UCv83tO5cePwHMt1952IVVHw	How to Build a Bert WordPiece Tokenizer in Python and HuggingFace	2021-09-13 20:13:08 UTC	2021-09-14 13:30:06 UTC	1880 seconds	Building a transformer model from scratch can often be the only option for many more specific use cases. Although BERT and other transformer models have been pre-trained for a vast number of languages and domains, they do not cover everything. Often, it is these less common use cases that stand to gain the most from having someone come along and build a specific transformer model. It could be for an uncommon language or less tech-savvy domain. BERT is the most popular transformer for a wide range of language-based machine learning - from sentiment analysis to question and answering, BERT has enabled a diverse range of innovation across many borders and industries. The first step for many in designing a new BERT model is the tokenizer. In this article, we'll take a look at the WordPiece tokenizer used by BERT - and see how we can build our own from scratch. 📕 Medium article: https://towardsdatascience.com/how-to-build-a-wordpiece-tokenizer-for-bert-f505d97dddbb 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🕹️ Free Article link (if you don't have Medium membership): https://towardsdatascience.com/how-to-build-a-wordpiece-tokenizer-for-bert-f505d97dddbb?sk=eea06e01c9faecd939e10589e9de1291	Science & Technology	95	1
H_kJDHvu-v8	UCv83tO5cePwHMt1952IVVHw	Metadata Filtering for Vector Search + Latest Filter Tech	2021-09-20 12:23:11 UTC	2021-09-20 14:04:27 UTC	2054 seconds	Vector similarity search makes massive datasets searchable in fractions of a second. Yet despite the brilliance and utility of this technology, often what seem to be the most straightforward problems are the most difficult to solve. Such as filtering. Filtering takes the top place in being seemingly simple — but actually incredibly complex. Applying fast-but-accurate filters when performing a vector search (ie, nearest-neighbor search) on massive datasets is a surprisingly stubborn problem. This article explains the two common methods for adding filters to vector search, and their serious limitations. Then we will explore Pinecone’s solution to filtering in vector search. 📣 Get the API key! https://www.pinecone.io/start/ 🌲 Pinecone article: https://www.pinecone.io/learn/vector-search-filtering/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:24 Vector Search Recap 02:03 Why Filter? 02:56 Metadata Filtering 101 07:48 Pre-filtering 09:37 Post-filtering 11:30 Single-Stage Filtering 12:22 Vectors and Metadata Code 13:58 Connecting to Pinecone 14:55 Building Query Vector 16:47 Querying 21:37 First Filter 24:40 Adding More Conditions 27:03 Filtering with Numbers 30:55 Search Speed and Filtering 33:44 Outro	Science & Technology	20	0
r-zQQ16wTCA	UCv83tO5cePwHMt1952IVVHw	Build NLP Pipelines with HuggingFace Datasets	2021-09-20 14:58:03 UTC	2021-09-23 13:30:07 UTC	2030 seconds	HF Datasets is an essential tool for NLP practitioners - hosting over 1.4K (mostly) high-quality language-focused datasets, and an easy-to-use treasure trove of functions for building efficient pre-processing pipelines. In this article, we will take a look at the massive repository of datasets available, and explore some of the library's brilliant data processing capabilities. 📕 Medium article: https://towardsdatascience.com/build-nlp-pipelines-with-huggingface-datasets-d597ff5f68ad 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 📖 Free Article Access (if you don't have Medium membership!): https://towardsdatascience.com/build-nlp-pipelines-with-huggingface-datasets-d597ff5f68ad?sk=948106e47e64bc3e9e8a1358b0568d48	Science & Technology	53	1
QvKMwLjdK-s	UCv83tO5cePwHMt1952IVVHw	HNSW for Vector Search Explained and Implemented with Faiss (Python)	2021-09-29 08:13:49 UTC	2021-10-05 13:00:23 UTC	2075 seconds	Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search. HNSW is a hugely popular technology that time and time again produces state-of-the-art performance with super-fast search speeds and flawless recall — HNSW is not to be missed. Despite being a popular and robust algorithm for approximate nearest neighbors (ANN) searches, understanding how it works is far from easy. This video helps demystify HNSW and explains this intelligent algorithm in an easy-to-understand way. Towards the end of the video, we'll look at how to implement HNSW using Faiss and which parameter settings give us the performance we need. 🌲 Pinecone article: https://www.pinecone.io/learn/hnsw/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://jamescalam.medium.com/subscribe (it's free!) https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:41 Foundations of HNSW 08:41 How HNSW Works 16:38 The Basics of HNSW in Faiss 21:40 How Faiss Builds an HNSW Graph 26.49 Building the Best HNSW Index 33:33 Fine-tuning HNSW 34:30 Outro	Science & Technology	131	3
g_yMowQikOE	UCv83tO5cePwHMt1952IVVHw	Intro to APIs in Python - API Series #1	2021-09-29 12:21:47 UTC	2021-09-29 14:00:18 UTC	1704 seconds	Taking those first steps into interacting with the web using Python can seem daunting - but it need not be. It is a surprisingly simple process, with well established rules and guidelines. We'll cover the absolute essentials for getting started, including: - Application Program Interfaces (APIs) - Javascript Object Notation (JSON) - Requests with Python - Real world use-cases 📕 Article: https://towardsdatascience.com/quick-fire-guide-to-apis-in-python-891dd98c8877 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://jamescalam.medium.com/subscribe (it's free!) https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 📖 Free Access Link (if you don't have Medium membership): https://towardsdatascience.com/quick-fire-guide-to-apis-in-python-891dd98c8877?sk=7c159ba45154db23abcc6a7f9de4f910 Geocoding Docs: https://developers.google.com/maps/documentation/geocoding/cloud-setup GitHub Docs: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token 00:00 Intro 00:20 What is an API? 01:47 RESTful APIs 05:26 API Methods 07:20 HTTP Codes (200s) 08:14 HTTP Codes (400s) 10:00 JSON Format 11:21 Talking to APIs in Python 14:30 Google Geocoding API 22:08 GitHub API 27:48 Outro	Science & Technology	119	0
bVZJ_O_-0RE	UCv83tO5cePwHMt1952IVVHw	Intro to Dense Vectors for NLP and Vision	2021-10-04 08:28:38 UTC	2021-10-12 17:47:15 UTC	2629 seconds	There is perhaps no greater component to the success of modern Natural Language Processing (NLP) technology than vector representations of language. The meteoric early 2010s rise of NLP was ignited with the introduction of word2vec by a team lead by Tomáš Mikolov in 2013. Word2vec is one of the most iconic and earliest examples of dense vectors representing text. But since the days of word2vec, developments in representing language have advanced at ludicrous speeds. This video will explore why we use dense vectors — and some of the best approaches to building dense vectors available today. 🌲 Pinecone article: https://www.pinecone.io/learn/dense-vector-embeddings-nlp/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:50 Why Dense Vectors? 03:55 Word2vec and Representing Meaning 08:40 Sentence Transformers 09:58 Sentence Transformers in Python 15:08 Question-Answering 18:18 DPR in Python 29:55 Vision Transformers 33:22 OpenAI's CLIP in Python 42:49 Review and What's Next	Science & Technology	92	0
MF75aNH3Gjs	UCv83tO5cePwHMt1952IVVHw	API Series #2 - Building an API with Flask in Python	2021-10-05 07:01:25 UTC	2021-10-07 14:52:32 UTC	1902 seconds	Next video - how to deploy to the cloud: https://youtu.be/3fsIcMgUOY8 How can we set up a way to communicate from one software instance to another? It sounds simple, and — to be completely honest — it is. All we need is an API. An API (Application Programming Interface) is a simple interface that defines the types of requests (demands/questions, etc.) that can be made, how they are made, and how they are processed. In our case, we will be building an API that allows us to send a range of GET/POST/PUT/PATCH/DELETE requests (more on this later), to different endpoints, and return or modify data connected to our API. We will be using the Flask framework to create our API and Insomnia to test it. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🕹️ Medium article: https://towardsdatascience.com/the-right-way-to-build-an-api-with-python-cd08ab285f8f 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP Free article link: https://towardsdatascience.com/the-right-way-to-build-an-api-with-python-cd08ab285f8f?sk=6e2dda4c8b6012767114e12ff34b1464 Download Insomnia: https://insomnia.rest/download	Science & Technology	117	2
WS1uVMGhlWQ	UCv83tO5cePwHMt1952IVVHw	Intro to Sentence Embeddings with Transformers	2021-10-19 09:44:58 UTC	2021-10-20 17:06:20 UTC	1866 seconds	Transformers have wholly rebuilt the landscape of natural language processing (NLP). Before transformers, we had okay translation and language classification thanks to recurrent neural nets (RNNs) — their language comprehension was limited and led to many minor mistakes, and coherence over larger chunks of text was practically impossible. Since the introduction of the first transformer model in the 2017 paper ‘Attention is all you need’, NLP has moved from RNNs to models like BERT and GPT. These new models can answer questions, write articles (maybe GPT-3 wrote this), enable incredibly intuitive semantic search — and much more. In this video, we will explore how these embeddings have been adapted and applied to a range of semantic similarity applications by using a new breed of transformers called ‘sentence transformers’. 🌲 Pinecone article: https://www.pinecone.io/learn/sentence-embeddings/ Vectors in ML: https://www.youtube.com/playlist?list=PLIUOU7oqGTLgz-BI8bNMVGwQxIMuQddJO 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP	Science & Technology	188	1
aSx0jg9ZILo	UCv83tO5cePwHMt1952IVVHw	Fine-tune Sentence Transformers the OG Way (with NLI Softmax loss)	2021-10-22 14:16:49 UTC	2021-10-22 14:39:46 UTC	2223 seconds	Sentence embeddings with transformers can be used across a range of applications, such as semantic textual similarity (STS), semantic clustering, or information retrieval (IR) using concepts rather than words. This video dives deeper into the training process of the first sentence transformer, sentence-BERT, or more commonly known as SBERT. We will explore the Natural Language Inference (NLI) training approach of softmax loss to fine-tune models for producing sentence embeddings. Be aware that softmax loss is no longer the preferred approach to training sentence transformers and has been superseded by other methods such as MSE margin and multiple negatives ranking loss. But we’re covering this training method as an important milestone in the development of ever-improving sentence embeddings. 🌲 Pinecone article: https://www.pinecone.io/learn/train-sentence-transformers-softmax/ Check out the Sentence Transformers library: https://github.com/UKPLab/sentence-transformers Talk by Nils Reimers (one of the SBERT creators) on training: https://www.youtube.com/watch?v=RHXZKUr8qOY He does more NLP vids too: https://www.youtube.com/channel/UC1zCuTrfpjT6Sv2kJk-JkvA 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:42 NLI Fine-tuning 01:44 Softmax Loss Training Overview 05:47 Preprocessing NLI Data 12:48 PyTorch Process 19:48 Using Sentence-Transformers 30:45 Results 35:49 Outro	Science & Technology	83	0
or5ew7dqA-c	UCv83tO5cePwHMt1952IVVHw	Fine-tune High Performance Sentence Transformers (with Multiple Negatives Ranking)	2021-10-25 20:18:30 UTC	2021-10-26 13:00:22 UTC	2213 seconds	Transformer-produced sentence embeddings have come a long way in a very short time. Starting with the slow but accurate similarity prediction of BERT cross-encoders, the world of sentence embeddings was ignited with the introduction of SBERT in 2019. Since then, many more sentence transformers have been introduced. These models quickly made the original SBERT obsolete. How did these newer sentence transformers manage to outperform SBERT so quickly? The answer is multiple negatives ranking (MNR) loss. This video will cover what MNR loss is, the data it requires, and how to implement it to fine-tune our own high-quality sentence transformers. Implementation will cover two approaches. The first is more involved, and outlines the exact steps to fine-tune the model (we'll just run over it quickly). The second approach makes use of the sentence-transformers library’s excellent utilities for fine-tuning. 🌲 Pinecone article: https://www.pinecone.io/learn/fine-tune-sentence-transformers-mnr/ Check out the Sentence Transformers library: https://github.com/UKPLab/sentence-transformers Talk by Nils Reimers (one of the SBERT creators) on training: https://www.youtube.com/watch?v=RHXZKUr8qOY He does more NLP vids too: https://www.youtube.com/channel/UC1zCuTrfpjT6Sv2kJk-JkvA 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:02 NLI Training Data 02:56 Preprocessing 10:11 SBERT Finetuning Visuals 14:14 MNR Loss Visual 16:37 MNR in PyTorch 23:04 MNR in Sentence Transformers 34:20 Results 36:14 Outro	Science & Technology	86	0
iCkftKsnQgg	UCv83tO5cePwHMt1952IVVHw	Hybrid Search Walkthrough in Pinecone	2021-10-29 01:44:06 UTC	2021-10-29 15:05:00 UTC	1040 seconds	Pinecone offers a production-ready vector database for high performance and reliable semantic search at scale. But did you know Pinecone's semantic search can be paired with the more traditional keyword search? Semantic search is a compelling technology allowing us to search using abstract concepts and meaning rather than relying on specific words. However, sometimes a simple keyword search can be just as valuable — especially if we know the exact wording of what we're searching for. In this video, we will explore these features through a start-to-finish example of basic keyword search in Pinecone. 🌲 Check the docs: https://www.pinecone.io/docs/examples/basic-hybrid-search/ 🔑 Free API key: https://app.pinecone.io 00:52 How Hybrid Search Works 01:25 Preprocessing 03:01 Creating Keywords 05:34 Creating an Index 06:50 Data Upsert 08:33 Query Setup 10:52 Keyword Search 12:31 OR Logic 14:49 AND Logic 15:10 Negation 17:04 Outro	Science & Technology	17	1
3fsIcMgUOY8	UCv83tO5cePwHMt1952IVVHw	API Series #3 - How to Deploy Flask APIs to the Cloud (GCP)	2021-11-01 23:16:31 UTC	2021-11-02 14:30:00 UTC	806 seconds	Building that first API is for many of us, a significant step towards creating impactful tools that may one day be used by many developers. But often those APIs don't make it out of our local machines. Fortunately, it's incredibly easy to deploy APIs. Assuming you have no idea what you're doing right now - you will probably be deploying your first API in around ten minutes. I'm not joking, it's super easy. Let's get started. 📕 Article: https://towardsdatascience.com/how-to-deploy-a-flask-api-8d54dd8d8b8a 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 📖 Free article link: TO ADD	Science & Technology	75	2
NNS5pOpjvAQ	UCv83tO5cePwHMt1952IVVHw	All You Need to Know on Multilingual Sentence Vectors (1 Model, 50+ Languages)	2021-11-04 11:27:18 UTC	2021-11-04 13:00:10 UTC	2392 seconds	We’ve learned about how sentence transformers can be used to create high-quality vector representations of text. We can then use these vectors to find similar vectors, which can be used for many applications such as semantic search or topic modeling. These models are very good at producing meaningful, information-dense vectors. But they don’t allow us to compare sentences across different languages. Often this may not be a problem. However, the world is becoming increasingly interconnected, and many companies span across multiple borders and languages. Naturally, there is a need for sentence vectors that are language agnostic. Unfortunately, very few textual similarity datasets span multiple languages, particularly for less common languages. And the standard training methods used for sentence transformers would require these types of datasets. Different approaches need to be used. Fortunately, some techniques allow us to extend models to other languages using more easily obtained language translations. In this video, we will cover how multilingual models work and are built. We’ll learn how to develop our own multilingual sentence transformers, the datasets to look for, and how to use high-performing pretrained multilingual models. 🌲 Pinecone article: https://www.pinecone.io/learn/multilingual-transformers/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:19 Multilingual Vectors 05:55 Multi-task Training (mUSE) 09:36 Multilingual Knowledge Distillation 11:13 Knowledge Distillation Training 13:43 Visual Walkthrough 14:53 Parallel Data Prep 20:23 Choosing a Student Model 24:55 Initializing the Models 30:05 ParallelSentencesDataset 33:54 Loss and Fine-tuning 36:59 Model Evaluation 39:23 Outro	Science & Technology	30	0
-td57YvJdHc	UCv83tO5cePwHMt1952IVVHw	Question-Answering in NLP (Extractive QA and Abstractive QA)	2021-11-13 19:09:02 UTC	2021-11-16 12:06:13 UTC	2886 seconds	Search is a crucial functionality in many applications and companies globally. Whether in manufacturing, finance, healthcare, or almost any other industry, organizations have vast internal information and document repositories. Unfortunately, the scale of many companies’ data means that the organization and accessibility of information can become incredibly inefficient. The problem is exacerbated for language-based information. Language is a tool for people to communicate often abstract ideas and concepts. Naturally, ideas and concepts are harder for a computer to comprehend and store in a meaningful way. How do we minimize this problem? The answer lies with semantic search, specifically with the question-answering (QA) flavor of semantic search. This article will introduce the different forms of QA, the components of these 'QA stacks', and where we might use them. 🌲 Pinecone article: https://www.pinecone.io/learn/question-answering/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Meaningful Search 01:23 Use-case 02:22 Open Domain QA (ODQA) 06:41 SQuAD Format 10:45 Quick Preprocessing 15:18 Creating Context Vectors Database 23:24 Open-book Extractive QA 32:50 Open-book Abstractive QA 41:53 Closed-book Abstractive QA 47:27 Final Thoughts	Science & Technology	72	0
pNvujJ1XyeQ	UCv83tO5cePwHMt1952IVVHw	Today Unsupervised Sentence Transformers, Tomorrow Skynet (how TSDAE works)	2021-11-24 14:20:20 UTC	2021-11-24 16:24:24 UTC	2661 seconds	To adapt a pretrained transformer to produce meaningful sentence vectors, we typically need a more supervised fine-tuning approach. We can use datasets like natural language inference (NLI) pairs, labeled semantic textual similarity (STS) data, or parallel data (pairs of translations). For some domains and languages, such as finance and English, this data is fairly easy to find or gather. But many domains and many languages have very little labeled data. If you can find semantic similarity pairs for the agriculture industry, please let me know. There are many languages, such as Dhivehi, where unlabelled data is hard to find and labelled data practically non-existent. This means you either spend a very long time gathering tens of thousands of labeled samples or you can try an unsupervised fine-tuning approach. Unsupervised training methods for sentence transformers are not as effective as their supervised counterparts, but they do work. And if you have no other choice, why not? In this video, we will introduce the concept of unsupervised fine-tuning for sentence transformers. We will learn to train these models using the unsupervised Transformer-based Sequential Denoising Auto-Encoder (TSDAE) approach. 🌲 Pinecone article: https://www.pinecone.io/learn/unsupervised-training-sentence-transformers/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Why Language Embedding Matters 05:12 Supervised Methods 05:29 Natural Language Inference 07:15 Semantic Textual Similarity 07:43 Multilingual Training 10:00 TSDAE (Unsupervised) 18:50 Data Preparation 29:05 Initialize Model 32:39 Model Training 36:25 NLTK Error 37:15 Evaluation 41:01 TSDAE vs Supervised Methods 42:42 Why TSDAE is Cool	Science & Technology	70	0
3IPCEeh4xTg	UCv83tO5cePwHMt1952IVVHw	Making The Most of Data: Augmented SBERT	2021-12-16 15:46:03 UTC	2021-12-17 14:24:40 UTC	3310 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp ML models are data-hungry. They consume massive amounts of data to identify generalized patterns and apply those learned patterns to new data. As models get bigger, so do datasets. And although we have seen an explosion of data in the past decade, it is often not accessible or in an ML-friendly format, especially in niche domains. For many niche, low-resource domains, finding or annotating a substantial dataset manually is practically impossible. Fortunately, we don't need to label (or even find) this new data. Instead, we can automatically generate or label data using one or more data augmentation techniques. In this video, we will introduce data augmentation and its application to the field of NLP. We will focus on the 'in-domain' flavor of a particular data-augmentation strategy named augmented SBERT (AugSBERT). 🌲 Pinecone article: https://www.pinecone.io/learn/data-augmentation/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP	Science & Technology	42	0
mjKqP3kRxbQ	UCv83tO5cePwHMt1952IVVHw	Building Transformer Tokenizers (Dhivehi NLP #1)	2021-12-28 15:02:22 UTC	2021-12-28 15:45:03 UTC	1982 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp Get in touch with Ashraq: https://www.linkedin.com/in/ismailashraq/ The language of Dhivehi (or Maldivian) is fascinating. It uses a complex writing system known as Thaana, and I absolutely cannot comprehend any of it. It is so wildly different from anything I know - but, like the archipelago, it looks wonderful. Ashraq described the difficulty of applying NLP to his native tongue of Dhivehi. There are several reasons for this, which we will explore in this video, and learn how to build an effective Dhivehi WordPiece tokenizer. 📕 Article: https://towardsdatascience.com/designing-tokenizers-for-low-resource-languages-7faa4ab30ef4 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Article Friend Link (Free Access): https://towardsdatascience.com/designing-tokenizers-for-low-resource-languages-7faa4ab30ef4?sk=c0c16de9eea7dbe1d2a9c106abf38e1a 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:06 Dhivehi Project 02:28 Hurdles for Low Resource Domains 04:21 Dhivehi Dataset 04:52 Download Dhivehi Corpus 08:25 Tokenizer Components 08:44 Normalizer Component 11:55 Pre-tokenization Component 14:59 Post-tokenization Component 16:26 Decoder Component 17:41 Tokenizer Implementation 21:04 Tokenizer Training 24:22 Post-processing Implementation 27:12 Decoder Implementation 28:07 Saving for Transformers 30:33 Tokenizer Test and Usage 31:36 Download Dhivehi Models 32:21 First Steps	Science & Technology	49	0
a8jyue22SJM	UCv83tO5cePwHMt1952IVVHw	AugSBERT: Domain Transfer for Sentence Transformers	2022-01-04 05:14:16 UTC	2022-01-04 14:59:50 UTC	1750 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp When building language models, we can spend months optimizing training and model parameters, but it’s useless if we don't have the correct data. The success of our language models relies first and foremost on data. The augmented SBERT training strategy can help us. Given this scenario, we can transfer information from an out-of-domain (or source) dataset to our target domain. We will learn how to do this here. First, we will learn to assess which source datasets align best with our target domain quickly. Then we will explain and work through the AugSBERT domain-transfer training strategy. 🌲 Pinecone article: https://www.pinecone.io/learn/augsbert-domain-transfer/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 🔗 n-gram Similarity Script: https://gist.github.com/jamescalam/b73f37017ae32bd6094747c4b0fca94a 🔗 AugSBERT In-Domain Article: https://www.pinecone.io/learn/data-augmentation/ 00:00 Why Use Domain Transfer 04:08 Strategy Outline 06:05 Train Source Cross-Encoder 12:44 Cross-Encoder Outcome 15:12 Labeling Target Data 20:31 Training Bi-encoder 23:58 Evaluator Bi-encoder Performance 28:08 Final Points	Science & Technology	41	0
w1dMEWm7jBc	UCv83tO5cePwHMt1952IVVHw	How to build a Q&A AI in Python (Open-domain Question-Answering)	2022-01-10 07:19:13 UTC	2022-01-11 14:00:20 UTC	2364 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp How can we design these natural, human-like Q&A interfaces? The answer is open-domain question-answering (ODQA). ODQA allows us to use natural language to query a database. That means that, given a dataset like a set of internal company documents, online documentation, or as is the case with Google, everything on the world’s internet, we can retrieve relevant information in a natural, more human way. 🌲 Pinecone article: https://www.pinecone.io/learn/retriever-models/ 🔗 Nils YT Talk: https://youtu.be/XNJThigyvos?t=118 🔗 MNR Loss Article: 🔗 Free Pinecone API Key: https://app.pinecone.io/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Why QA 04:05 Open Domain QA 08:24 Do we need to fine-tune? 11:44 How Retriever Training Works 12:59 SQuAD Training Data 16:29 Retriever Fine-tuning 19:32 IR Evaluation 25:58 Vector Database Setup 33:42 Querying 37:41 Final Notes	Science & Technology	66	1
-fzCSPsfMic	UCv83tO5cePwHMt1952IVVHw	How to build a Q&A Reader Model in Python (Open-domain QA)	2022-01-18 12:17:09 UTC	2022-01-18 16:37:37 UTC	1504 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp Open-domain question-answering (ODQA) is a wildly popular pipeline of databases and language models that allow us to ask a machine human-like questions and return comprehensible and even intelligent answers. Despite the outward guise of simplicity, ODQA requires a reasonably advanced set of components placed together to enable the extractive Q&A functionality. We call this extractive Q&A because the models are not generating an answer. Instead, the answer already exists but is hidden somewhere within potentially thousands, millions, or even more data sources. By enabling extractive Q&A, we enable a more intelligent and efficient way to retrieve information from what can be massive stores of data. 🌲 Pinecone article: https://www.pinecone.io/learn/reader-models/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:13 ODQA Components 03:09 Data Preprocessing 22:35 Fine-tuning	Science & Technology	26	0
JLKUV-LiXjk	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #1 - Installation and API	2022-01-25 12:04:00 UTC	2022-01-25 16:00:09 UTC	735 seconds	▶️ Streamlit for ML Part 2: https://www.youtube.com/watch?v=U0EoaFFGyTg&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=2 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-larger organizations. All of this means that there is no better time to pick up some experience with Streamlit. Fortunately, the basics of Streamlit are incredibly easy to learn, and for most tools, this will be more than you need! In this series, we will introduce Streamlit by building a general knowledge Q&A interface. We will learn about key Streamlit components like write, text_input, container. How to use external libraries like Bootstrap to quickly create new app components. And use caching to speed up our app. 📕 Article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec?sk=ac5e0b7c39938f52162862411a66a58b 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:39 App Outline 03:36 Streamlit Installation 06:15 Streamlit API Basics	Science & Technology	32	0
U0EoaFFGyTg	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #2 - ML Models and APIs	2022-01-26 16:07:51 UTC	2022-01-26 16:30:36 UTC	911 seconds	▶️ Streamlit for ML Part 3: https://www.youtube.com/watch?v=lYDiSCDcxmc&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=3 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-larger organizations. All of this means that there is no better time to pick up some experience with Streamlit. Fortunately, the basics of Streamlit are incredibly easy to learn, and for most tools, this will be more than you need! In this series, we will introduce Streamlit by building a general knowledge Q&A interface. We will learn about key Streamlit components like write, text_input, container. How to use external libraries like Bootstrap to quickly create new app components. And use caching to speed up our app. 🔗 Code to Create Index: https://gist.github.com/jamescalam/2123ce0bb8a871f48a151a023a7ece67 📕 Article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec?sk=ac5e0b7c39938f52162862411a66a58b 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:47 Creating the Vector DB 08:56 Implementing Retrieval	Science & Technology	19	0
lYDiSCDcxmc	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #3 - Make Apps Fast with Caching	2022-01-27 13:13:14 UTC	2022-01-27 15:00:36 UTC	584 seconds	▶️ Streamlit for ML Part 4: https://www.youtube.com/watch?v=XdxeKiY2UXg&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=4 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-larger organizations. All of this means that there is no better time to pick up some experience with Streamlit. Fortunately, the basics of Streamlit are incredibly easy to learn, and for most tools, this will be more than you need! In this series, we will introduce Streamlit by building a general knowledge Q&A interface. We will learn about key Streamlit components like write, text_input, container. How to use external libraries like Bootstrap to quickly create new app components. And use caching to speed up our app. ▶️ Streamlit for ML Playlist: https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1 📕 Article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec?sk=ac5e0b7c39938f52162862411a66a58b 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 02:35 Streamlit Caching 06:56 Experimental Caching Primitives	Science & Technology	24	0
XdxeKiY2UXg	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #4 - Adding Bootstrap Components	2022-01-28 10:05:43 UTC	2022-01-28 15:11:42 UTC	590 seconds	▶️ Streamlit for ML Part 5.1: https://www.youtube.com/watch?v=SGazDb8o-to&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=5 Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-larger organizations. All of this means that there is no better time to pick up some experience with Streamlit. Fortunately, the basics of Streamlit are incredibly easy to learn, and for most tools, this will be more than you need! In this series, we will introduce Streamlit by building a general knowledge Q&A interface. We will learn about key Streamlit components like write, text_input, container. How to use external libraries like Bootstrap to quickly create new app components. And use caching to speed up our app. ▶️ Streamlit for ML Playlist: https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1 📕 Article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec?sk=ac5e0b7c39938f52162862411a66a58b 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 02:35 Streamlit Caching 06:56 Experimental Caching Primitives	Science & Technology	38	1
JydpRavoJqI	UCv83tO5cePwHMt1952IVVHw	Adding New Doc Stores to Haystack	2022-02-15 04:56:36 UTC	2022-03-15 15:00:14 UTC	1825 seconds	🥳 Released with Haystack v1.3! Install direct from PyPI with: pip install 'farm-haystack[pinecone]' PR: https://github.com/deepset-ai/haystack/pull/2254 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 02:15 Contributing or Testing 03:31 ODQA 06:20 What is Haystack? 08:13 Haystack QA Workflow 14:52 Contributing to Open Source 22:54 Haystack Doc Stores 26:09 Doc Store Core Methods 29:31 Final Notes, Contribute/Test	Science & Technology	14	0
SGazDb8o-to	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #5.1 - Custom React Components in Streamlit Setup	2022-02-17 15:24:47 UTC	2022-02-17 15:45:58 UTC	1158 seconds	▶️ Streamlit for ML Part 5.2: https://www.youtube.com/watch?v=mxm8ihWoVbk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=6 There are plenty of prebuilt components designed by Streamlit themselves, and if you can't find what you need, there are even community-built components. If you're still stuck, and there is just no component that covers what you need, we can build our own custom components. To do this we do need to start playing with the lower-level web technologies that Streamlit itself is built upon. So it isn't as simple as using a prebuilt component. However, thanks to pre-made templates, it isn't too hard to create a new component. In this sub-series, we'll learn exactly how to create custom components. We'll focus on designing an interactive card component using Material UI design elements. ▶️ Streamlit for ML Playlist: https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1 📕 Article: Coming soon 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: Coming soon 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 02:19 Environment Setup 03:42 Starting with a Template 07:41 Naming for Card Component 11:31 Installing Node Packages 15:12 Running the Component	Science & Technology	26	1
mxm8ihWoVbk	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #5.2 - MUI Card Component Build	2022-02-20 15:25:56 UTC	2022-02-21 14:00:31 UTC	1619 seconds	▶️ Streamlit for ML Part 5.3: https://www.youtube.com/watch?v=lZ2EaPUnV7k&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=7 There are plenty of prebuilt components designed by Streamlit themselves, and if you can't find what you need, there are even community-built components. If you're still stuck, and there is just no component that covers what you need, we can build our own custom components. To do this we do need to start playing with the lower-level web technologies that Streamlit itself is built upon. So it isn't as simple as using a prebuilt component. However, thanks to pre-made templates, it isn't too hard to create a new component. In this sub-series, we'll learn exactly how to create custom components. We'll focus on designing an interactive card component using Material UI design elements. ▶️ Streamlit for ML Playlist: https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1 📕 Article: Coming soon 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: Coming soon 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:59 Clearing Card Component 04:59 Building the Component 14:22 Pulling in MUI Code 24:08 Adding Roboto Font 26:05 Final Points	Science & Technology	16	1
lZ2EaPUnV7k	UCv83tO5cePwHMt1952IVVHw	Streamlit for ML #5.3 - Publishing Components to Pip	2022-02-27 16:28:49 UTC	2022-02-28 17:00:29 UTC	858 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp There are plenty of prebuilt components designed by Streamlit themselves, and if you can't find what you need, there are even community-built components. If you're still stuck, and there is just no component that covers what you need, we can build our own custom components. To do this we do need to start playing with the lower-level web technologies that Streamlit itself is built upon. So it isn't as simple as using a prebuilt component. However, thanks to pre-made templates, it isn't too hard to create a new component. In this sub-series, we'll learn exactly how to create custom components. We'll focus on designing an interactive card component using Material UI design elements. ❗ Python Packaging Video: https://youtu.be/JkeNVaiUq_c ▶️ Streamlit for ML Playlist: https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1 📕 Article: Coming soon 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 📖 Friend link to article: Coming soon 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:09 PyPI 02:41 Preparing for Distribution 05:43 Build React Component 06:39 Create Python Package 11:57 Pip Install 13:58 Ending	Science & Technology	10	0
J0cntjLKpmU	UCv83tO5cePwHMt1952IVVHw	Train Sentence Transformers by Generating Queries (GenQ)	2022-03-08 03:10:28 UTC	2022-03-08 14:52:23 UTC	1634 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp Fine-tuning effective dense retrieval models is challenging. Bi-encoders (sentence transformers) are the current best models for dense retrieval in semantic search. Unfortunately, they're also notoriously data-hungry models that typically require a particular type of labeled training data. Hard problems like this attract attention. As expected, there is plenty of attention on building ever better techniques for training retrievers. One of the most impressive is GenQ. This approach to building bi-encoder retrievers uses the latest text generation techniques to synthetically generate training data. In short, all we need are passages of text. The generation model then augments these passages with synthetic queries, giving us the exact format we need to train an effective bi-encoder model. 🌲 Pinecone article: https://www.pinecone.io/learn/genq/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 00:32 Why GenQ? 02:23 GenQ Overview 04:28 Training Data 06:48 Asymmetric Semantic Search 07:54 T5 Query Generation 13:52 Finetuning Bi-encoders 16:02 GenQ Code Walkthrough 21:40 Finetuning Bi-encoder Walkthrough 26:48 Final Points	Science & Technology	39	0
Dn8OYkatiU0	UCv83tO5cePwHMt1952IVVHw	Testing the New Haystack Doc Store	2022-03-22 17:15:10 UTC	2022-03-22 19:26:00 UTC	1399 seconds	🥳 Released with Haystack v1.3! Install direct from PyPI with: pip install 'farm-haystack[pinecone]' PR: https://github.com/deepset-ai/haystack/pull/2254 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:19 Demo Start and Install 03:25 Initialization 06:30 Download and Write Documents 10:55 Extractive QA Pipeline 11:23 Fetch by ID 19:01 Metadata Filtering 22:24 Get All Documents	Science & Technology	5	0
uEbCXwInnPs	UCv83tO5cePwHMt1952IVVHw	Is GPL the Future of Sentence Transformers? \| Generative Pseudo-Labeling Deep Dive	2022-03-29 10:46:39 UTC	2022-03-30 12:52:39 UTC	3175 seconds	🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp Training sentence transformers is hard; they need vast amounts of labeled data. On one hand, the internet is full of data, and, on the other, this data is not in the format we need. We usually need to use a supervised training method to train a high-performance bi-encoder (sentence transformer) model. There is research producing techniques placing us ever closer to fine-tuning high-perfomance bi-encoder models with unlabeled text data. One of the most promising is GPL. At its core, GPL allows us to take unstructured text data and use it to build models that can understand this text. These models can then intelligently respond to natural language queries regarding this same text data. It is a fascinating approach, with massive potential across innumerous use cases spanning all industries and borders. With that in mind, let's dive into the details of GPL and how we can implement it to build high-performance LMs with nothing more than plain text. 🌲 Pinecone article: https://www.pinecone.io/learn/gpl/ 🔗 Notebooks: https://github.com/pinecone-io/examples/tree/master/learn/nlp_course/gpl 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:08 Semantic Web and Other Uses 04:36 Why GPL? 07:31 How GPL Works 10:37 Query Generation 12:08 CORD-19 Dataset and Download 13:27 Query Generation Code 21:53 Query Generation is Not Perfect 22:39 Negative Mining 26:28 Negative Mining Implementation 27:21 Negative Mining Code 35:19 Pseudo-Labeling 35:55 Pseudo-Labeling Code 37:01 Importance of Pseudo-Labeling 41:20 Margin MSE Loss 43:40 MarginMSE Fine-tune Code 46:30 Choosing Number of Steps 48:54 Fast Evaluation 51:43 What's Next for Sentence Transformers?	Science & Technology	76	2