gavinkhung/machine-learning-visualized
gavinkhung/machine-learning-visualized has 2,101 GitHub stars and 199 forks. GitHot measured +2 stars in 24 hours, +6 in 7 days, and +9 in 15 days. Last checked 2026-11-12 20:19 UTC. Last push 2026-08-19.
Jupyter NotebookとNumPyを使って機械学習アルゴリズムを実装し、第一原理から導出しています。
計測したスター増加
- 24時間
- +2
- 7日間
- +6
- 15日間
- +9
- 1か月
- 履歴を収集中
期間ごとの純増数です。期間は重複しており、日別の値ではありません。未計測の値は空欄です。スター数は注目度の指標であり、品質の保証ではありません。
README.md
オリジナルMachine Learning Visualized

URL: https://ml-visualized.com/
Machine Learning Visualized is a Jupyter Book containing Jupyter Notebooks that implement and mathematically derive machine learning algorithms from first-principles.
There are also Interactive Notebooks built with Marimo that allow you to see how the weights influence the loss functions.
The output of each notebook is a visualization of the machine learning algorithm throughout its training phase, ultimately converging at its optimal weights.
There is a separate Github Repository for each machine learning algorithm. Thus, this repository is simply the code to configure and build the Jupyter Book. At a very high level, Jupyter Books allow you to build a website with Markdown files and Jupyter Notebooks. Notice that none of the Jupyter Notebooks are in this repository. There is a SH script to download the relevant Jupyter Notebooks from other Github Repos. Once that is complete, the Jupyter Book can be built. The website is updated using the GitHub Action at .github/workflows/ci.yml after every commit or pull request. To build the website locally, see the Usage section below.
Jupyter Notebooks
- Neural Networks Repo
- Autoencoder Repo
- Logistic Regression Repo
- Perceptron Repo
- Principal Component Analysis Repo
- K Means Repo
- Gradient Descent Repo
Jupyter Book Info
Table of Contents and structure of the book is specified at _toc.yml.
Configuration is specified at _config.yml.
For more information, check out the Jupyter Book Docs.
Usage
Step 1: Download the Jupyter Notebooks
chmod +x ./download_notebooks.sh
./download_notebooks.sh
Step 2: Building the Jupyter Book
Option 1: jupyter-book CLI
pip install -U jupyter-book
jupyter-book build .
Option 2: Docker Compose
docker compose run --rm jupyter-book
docker compose down --remove-orphans --volumes --rmi local
Option 3: Docker
docker build -f Dockerfile.book -t jupyter-book .
docker run --rm -v "$(pwd)":/usr/src/app jupyter-book
docker stop jupyter-book
docker rm jupyter-book
docker rmi jupyter-book
Step 3: Open the Jupyter Book
Navigate to _build/html/index.html
Build EPUB (NEW)
brew install --cask mactex
nbmerge $(ls chapter1/*.ipynb chapter2/*.ipynb chapter3/*.ipynb chapter4/*.ipynb | sort) -o book/combined.ipynb
jupyter nbconvert --to latex book/combined.ipynb
docker build -f Dockerfile.pandoc -t my-pandoc .
docker run --rm -v $(pwd):/data my-pandoc pandoc book/combined.tex -o book/combined.epub --mathml --embed-resources --standalone
Output
Marimo Interactive Notebooks

Mathematically Explained

関連プロジェクトを探す
tensorflow/tensorflow
すべての人のためのオープンソース機械学習フレームワーク
201,584 スターhuggingface/transformers
🤗 Transformers:テキスト、ビジョン、音声、マルチモーダルの最先端機械学習モデル向けモデル定義フレームワークで、推論と学習の両方に対応します。
168,917 スターAUTOMATIC1111/stable-diffusion-webui
Stable Diffusion の Web UI
165,300 スターComfy-Org/ComfyUI
グラフとノードによるインターフェースを備えた、最も強力でモジュール性の高い拡散モデル向け GUI、API、バックエンド。世界最速のローカル推論エンジン。
146,472 スター