rasbt/deep-learning-book resource

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2820 stars · 739 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Companion repository for the book 'Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python', containing early-access manuscript drafts, hands-on code examples, exercise solutions, and math/NumPy/PyTorch/TensorFlow appendix material. Content is primarily delivered as Jupyter notebooks implementing deep learning algorithms in PyTorch.

Use cases

• learn deep learning and neural networks from scratch with python
• study gradient descent, backpropagation, and cost function optimization with code
• understand CNNs, RNNs, autoencoders, and GANs through runnable notebook examples
• find hands-on exercises and solutions while learning pytorch for machine learning
• brush up on linear algebra, calculus, and numpy before starting deep learning
• get a practical introduction to perceptrons and softmax regression

When to choose

• You want theory paired with executable PyTorch notebooks rather than just reading
• You are self-studying deep learning fundamentals and want exercises with solutions
• You need refresher appendices on math notation, NumPy, and PyTorch basics

When to avoid

• You need a production deep-learning framework or maintained codebase - this is educational material with development largely stopped around 2020
• You want TensorFlow 2.x as the primary framework - the main text centers on PyTorch and older TensorFlow material is archived
• You need a finished, published textbook - the manuscripts remain early-access drafts

Facets

learning-resource · maturity maintenance

machine-learning deep-learning data-science reinforcement-learning artificial-intelligence machine-learning deep-learning data-science tutorials python cross-platform jupyter-notebooks textbook pytorch tensorflow neural-networks education exercise-solutions

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