Jeremy London

jeremy-london

AI & ML interests

LSTM, Multimodal AI, synthetic dataset generation, fine-tuning Large Language Models (LLM) for agent tasks, knowledge graphs, system-level AI opportunities, recommendation systems, feature extraction, classification, embedding searches, NER (Named Entity Recognition), generative modeling

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reacted to TuringsSolutions's post with ๐Ÿ‘ about 2 months ago
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1825
Neural Network Chaos Monkey: Randomly shuts off parts of the neural network during training. The Chaos Monkey is super present at Epoch 1, is gone by the final Epoch. My hypothesis was that this would either increase the robustness of the model, or it would make the outputs totally worse. You can 100% reproduce my results, chaos wins again.

https://youtu.be/bWA9unotJ7k
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reacted to yeonseok-zeticai's post with ๐Ÿคโค๏ธ๐Ÿค—๐Ÿ˜Ž๐Ÿ‘๐Ÿš€ 2 months ago
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1827
๐Ÿš€ Revolutionary Method to Convert YOLOv8 to On-Device AI with mobile NPU utilizations

Attention AI developers and engineers!
Discover how ZETIC.MLange can effortlessly transform YOLOv8 models into on-device AI with mobile NPU utilizations. ๐ŸŽ‰

๐Ÿ’ก We highlight the power of mobile NPU, showing how it outperforms CPU in processing speed. The results speak for themselvesโ€”NPU-driven execution is faster, smarter, and more efficient.
* Real-time demos are no easy feat but with ZETIC.MLange, weโ€™ve made it possible!

๐ŸŽฅ Watch the video to see how weโ€™re revolutionizing on-device AI.
: https://youtu.be/LkP3JDTcVN8?si=6Ha5vHA-G7jZE9oq

๐ŸŒŸ Our team has successfully implemented YOLOv8 as on-device AI using ZETIC.MLange. This innovative approach enables high-performance object detection across various manufacturers mobile devices.

๐Ÿ” Curious about the details? We've shared a comprehensive guide on our blog. Check it out through the link below!
๐Ÿ“š Blog link: https://zetic.ai/blog/implementing-yolov8-on-device-ai-with-zetic-mlange