oreilly-japan/deep-learning-from-scratch resource

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4839 stars · 3558 forks observed · 2026-08-28

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

Companion source code repository for the O'Reilly Japan book 'Deep Learning from Scratch' (2016), organized by chapter with Python/NumPy implementations of neural networks. Includes Jupyter notebooks runnable on Amazon SageMaker Studio Lab.

Use cases

• learn deep learning from scratch in python
• implement neural networks with numpy only
• study backpropagation and gradient descent step by step
• find companion code for the deep learning from scratch book
• run beginner deep learning notebooks in the cloud

When to choose

• you want to understand deep learning fundamentals by implementing everything from scratch without frameworks
• you are following the O'Reilly book and need its code
• you prefer minimal NumPy/Matplotlib examples over heavy frameworks

When to avoid

• you need production-ready or GPU-accelerated deep learning with PyTorch or TensorFlow
• you want modern architectures like transformers or large-scale training
• you need maintained library APIs rather than educational code

Facets

learning-resource · maturity stable

deep-learning machine-learning data-science deep-learning machine-learning tutorials education python cross-platform book-companion-code jupyter-notebook numpy neural-networks educational japanese

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