gavinkhung/machine-learning-visualized resource

Health v2 · maintenance only

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1929 stars · 179 forks observed · 2026-08-28

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

A Jupyter Book website that implements and mathematically derives machine learning algorithms from first principles using Jupyter Notebooks and NumPy, with visualizations of training convergence. It aggregates notebooks from separate per-algorithm repositories and includes interactive Marimo notebooks for exploring weights and loss functions.

Use cases

• learn how neural networks work from first principles
• visualize gradient descent converging on optimal weights
• understand backpropagation math step by step
• study logistic regression and perceptron derivations
• see interactive loss landscape visualizations
• build an EPUB or website of ML algorithm notebooks
• learn PCA and k-means clustering with visual walkthroughs

When to choose

• you want to learn ML algorithms mathematically rather than via high-level libraries
• you prefer visual, notebook-based explanations of training dynamics
• you want NumPy-only implementations without framework abstractions
• you need a free open-source ML textbook with interactive elements

When to avoid

• you need production-ready ML code or a reusable library
• you want GPU-accelerated or deep learning framework tooling like PyTorch
• you need comprehensive coverage of modern topics like transformers or LLMs

Facets

learning-resource · maturity active

machine-learning data-visualization developer-tools machine-learning deep-learning tutorials education data-visualization python cross-platform jupyter-book jupyter-notebooks numpy first-principles neural-networks interactive-notebooks marimo epub web-server

Member repositories

For agents

markdown · JSON · MCP: product_card(name="gavinkhung/machine-learning-visualized")