rasbt/deeplearning-models resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

17588 stars · 4107 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A curated collection of deep learning architectures, models, and tips implemented as Jupyter Notebooks in both PyTorch and TensorFlow. It covers everything from basic machine learning algorithms like perceptrons to CNNs and other neural network architectures, with many examples provided in multiple framework variants.

Use cases

• learn how to implement neural networks from scratch
• compare PyTorch and TensorFlow implementations of the same model
• find reference code for CNN architectures
• study deep learning tips like dropout and batch normalization
• get started with PyTorch Lightning examples
• understand backpropagation with a from-scratch implementation

When to choose

• you want side-by-side PyTorch and TensorFlow code for the same architecture
• you are learning deep learning and prefer runnable notebooks
• you need clean, educational reference implementations rather than production libraries

When to avoid

• you need a production-ready model library or pip-installable package
• you want the latest state-of-the-art architectures
• you need maintained, actively updated code for current framework versions

Facets

learning-resource · maturity maintenance

deep-learning machine-learning developer-tools deep-learning machine-learning tutorials computer-vision python cross-platform jupyter-notebooks pytorch tensorflow pytorch-lightning educational neural-networks model-architectures natural-language-processing

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markdown · JSON · MCP: product_card(name="rasbt/deeplearning-models")