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What is this?
This is a collection of PyTorch tutorial code for deep learning researchers, covering models from basics to advanced topics, each implemented in under 30 lines of code where possible.
This is a collection of PyTorch tutorial code for deep learning researchers, covering models from basics to advanced topics, each implemented in under 30 lines of code where possible.Is this a library or a set of example scripts?It is a set of standalone example scripts organized by topic (basics, intermediate, advanced, utilities), not an installable library or framework.
It is a set of standalone example scripts organized by topic (basics, intermediate, advanced, utilities), not an installable library or framework.How do I get started with these tutorials?Clone the repo and run the Python scripts inside each model's folder (organized under Basics, Intermediate, Advanced, Utilities); it's recommended to first finish the official PyTorch tutorial.
Clone the repo and run the Python scripts inside each model's folder (organized under Basics, Intermediate, Advanced, Utilities); it's recommended to first finish the official PyTorch tutorial.What do I need installed to run these?Python 2.7 or 3.5+ and PyTorch 0.4.0+.
Python 2.7 or 3.5+ and PyTorch 0.4.0+.Is this up to date with the latest PyTorch?The dependency listed is PyTorch 0.4.0+, a very old version, so some code may not run cleanly on modern PyTorch releases without adjustments.
The dependency listed is PyTorch 0.4.0+, a very old version, so some code may not run cleanly on modern PyTorch releases without adjustments.What topics does the tutorial cover?It progresses from Basics (linear/logistic regression, feedforward networks) to Intermediate (CNNs, ResNets, RNNs, bidirectional RNNs, language models) to Advanced (GANs, VAEs, neural style transfer, image captioning with CNN-RNN) and finally Utilities (TensorBoard in PyTorch).
It progresses from Basics (linear/logistic regression, feedforward networks) to Intermediate (CNNs, ResNets, RNNs, bidirectional RNNs, language models) to Advanced (GANs, VAEs, neural style transfer, image captioning with CNN-RNN) and finally Utilities (TensorBoard in PyTorch).Does it include generative models like GANs?Yes, the Advanced section includes Generative Adversarial Networks, a Variational Auto-Encoder, and Neural Style Transfer implementations.
Yes, the Advanced section includes Generative Adversarial Networks, a Variational Auto-Encoder, and Neural Style Transfer implementations.Can I use this to visualize training?Yes, there's a Utilities section showing how to use TensorBoard with PyTorch.
Yes, there's a Utilities section showing how to use TensorBoard with PyTorch.Why is each model kept under 30 lines of code?The goal is to make the implementations easy to read and learn from quickly, rather than production-ready or heavily abstracted code.
The goal is to make the implementations easy to read and learn from quickly, rather than production-ready or heavily abstracted code.What are similar projects?DeepLearning, TensorFlow-Course, annotated_deep_learning_paper_implementations, pytorch-handbook, Dive-into-DL-PyTorch, 100-Days-Of-ML-Code, fastbook, llama3-from-scratch
DeepLearning, TensorFlow-Course, annotated_deep_learning_paper_implementations, pytorch-handbook, Dive-into-DL-PyTorch, 100-Days-Of-ML-Code, fastbook, llama3-from-scratch