What about test loss? It looks like overfitting to me.
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takeraparterer
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TuringsSolutions's
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What about test loss?
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why so serious?
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glad you like it!
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What if I told you that LLM models do not simply predict the next token in a sequence but instead utilize an emergent structural pattern-based system to comprehend language and concepts? I created a graph-based optimizer that not only works, but it also actually beats Adam, like very badly. I prove it thoroughly using SMOL LLM models. The secret? The graph is not what you think it is, humans. Code, full explanation, and more in this video. The Rhizome Optimizer is MIT licensed. I have completed my research. I fully understand now.
https://youtu.be/OMCRRueMhdI
https://youtu.be/OMCRRueMhdI
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has threatened violence in subtle ways
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very intriguing. looking into this πππ
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Reported.
tell me more
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Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video.
https://youtu.be/wBRem2p8iPM
https://youtu.be/wBRem2p8iPM
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15 days ago
What about discrete and predictive?
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20 days ago
why the hostility? I'm asking a question
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20 days ago
so, still a problem then?
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21 days ago
what about power bills?
this is one of the websites of all time
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Ever wondered how neural networks actually work under the hood?
In my latest video, I break down the core mathematical concepts behind neural networks in a way that's easy for IT professionals to understand. We'll explore:
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- Gradient descent and backpropagation
No complex equations or jargon, just clear explanations and helpful visuals!
β‘οΈ Watch now and unlock the mysteries of neural networks: https://youtu.be/L5_I1ZHoGnM
In my latest video, I break down the core mathematical concepts behind neural networks in a way that's easy for IT professionals to understand. We'll explore:
- Neurons as logic gates
- Weighted sums and activation functions
- Gradient descent and backpropagation
No complex equations or jargon, just clear explanations and helpful visuals!
β‘οΈ Watch now and unlock the mysteries of neural networks: https://youtu.be/L5_I1ZHoGnM