pytorch-symbolic
machine-learning-book
| pytorch-symbolic | machine-learning-book | |
|---|---|---|
| 3 | 2 | |
| 57 | 2,878 | |
| - | - | |
| 4.7 | 6.8 | |
| about 1 year ago | 10 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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pytorch-symbolic
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Package for visualizing pytorch models
Question: will your library work with pytorch functional api ?
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Pytorch Symbolic: an equivalent of Keras Functional API
See on GitHub
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Functional API for PyTorch - easier and quicker way to create models
You should be able to create any acyclic network with this API, including multiple input/output ones. For more information go to github and if you face any problems, please leave an Issue there.
machine-learning-book
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Implementing a ChatGPT-like LLM from scratch, step by step
Sorry, in that case I would rather recommend a dedicated RL book. The RL part in LLMs will be very specific to LLMs, and I will only cover what's absolutely relevant in terms of background info. I do have a longish intro chapter on RL in my other general ML/DL book (https://github.com/rasbt/machine-learning-book/tree/main/ch1...) but like others said, I would recommend a dedicated RL book in your case.
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"Machine Learning with PyTorch and Scikit-Learn" book
All the code examples are available here: https://github.com/rasbt/machine-learning-book
What are some alternatives?
torchview - torchview: visualize pytorch models
skorch - A scikit-learn compatible neural network library that wraps PyTorch
python-machine-learning-book-3rd-edition - The "Python Machine Learning (3rd edition)" book code repository
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
embedding-encoder - Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
gdrl - Grokking Deep Reinforcement Learning
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠