iamtrask/Grokking-Deep-Learning resource

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7722 stars · 1624 forks observed · 2026-08-28

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

A collection of Jupyter Notebook chapters accompanying the book 'Grokking Deep Learning' by Andrew Trask. It teaches deep learning fundamentals from scratch, covering forward propagation, gradient descent, backpropagation, CNNs, RNNs, LSTMs, and automatic differentiation.

Use cases

• learn deep learning from scratch
• understand how backpropagation works
• build a neural network without frameworks
• study gradient descent with worked examples
• learn about LSTMs and RNNs
• understand automatic differentiation by building a mini framework

When to choose

• you want to learn deep learning concepts from first principles
• you prefer code-driven tutorials in Jupyter notebooks
• you want to understand internals before using frameworks like PyTorch

When to avoid

• you need a production deep learning library
• you want state-of-the-art model implementations
• you need a maintained, licensed codebase for reuse

Facets

learning-resource · maturity maintenance

deep-learning machine-learning deep-learning machine-learning tutorials python cross-platform jupyter-notebooks book-companion neural-networks gradient-descent backpropagation rnn lstm cnn word-embeddings education

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