MingchaoZhu/DeepLearning resource

Health v2 · maintenance only

Adoption not part of the score

7744 stars · 1457 forks observed · 2026-08-28

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

A Chinese-language companion project to the 'Deep Learning' (flower book) textbook, re-explaining concepts via mathematical derivations and implementing them from scratch in Python with NumPy. It ships as chapter PDFs plus source-level code covering topics like regularization, ensemble learning (XGBoost, GBDT), and Bayesian methods.

Use cases

• learn deep learning from mathematical first principles
• understand the math behind the Deep Learning book
• implement neural network algorithms from scratch in numpy
• study how xgboost and gbdt work internally
• find derivations for regularization and bayesian regression
• prepare for machine learning interviews

When to choose

• you want source-level, framework-free implementations to deeply understand algorithms
• you prefer Chinese-language explanations with detailed derivations
• you are studying the Goodfellow Deep Learning book and need supplementary material

When to avoid

• you need a production deep learning framework like PyTorch or TensorFlow
• you want English-language tutorials
• you need actively maintained code for current research models

Facets

learning-resource · maturity maintenance

machine-learning deep-learning math deep-learning machine-learning tutorials education python numpy from-scratch-implementations textbook-companion chinese-language xgboost bayesian-methods regularization

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