@XAMTO_AI: What's the biggest trap in machine learning for beginners? Either the tutorials are full of fluff and leave you still confused, or they just throw code at you and you run it without understanding why. There's a gem on GitHub: Machine Learning Visualized, which directly visualizes the algorithm training process - showing how weights are updated, how they converge, etc.
Summary
Introduces the Machine Learning Visualized project on GitHub. This tool uses interactive notebooks and visualizations to show the training process of machine learning algorithms (e.g., neural networks, logistic regression, etc.), helping beginners understand the principles.
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What are the most common pitfalls in machine learning for beginners?
Either the tutorials are full of fluff, leaving you confused after listening; or they jump straight into code, and you run it without understanding why it works.
There’s a hidden gem on GitHub: Machine Learning Visualized, which directly visualizes the training process of algorithms for you to see — how weights are updated, how convergence happens, all laid out transparently.
What you get:
- Complete implementations of neural networks, logistic regression, and perceptron
- Derived from first principles, with no skipped steps in formulas
- Interactive notebooks, adjust parameters and see results immediately
- Covers PCA, K-means, gradient descent
- Supports online viewing and one-click local deployment via Docker
https://github.com/gavinkhung/machine-learning-visualized…
gavinkhung/machine-learning-visualized
Source: https://github.com/gavinkhung/machine-learning-visualized
Machine Learning Visualized
website
URL: https://ml-visualized.com/
Machine Learning Visualized is a Jupyter Book (https://jupyterbook.org/en/stable/intro.html) 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 (https://github.com/gavinkhung/neural-network)
- Logistic Regression Repo (https://github.com/gavinkhung/logistic-regression)
- Perceptron Repo (https://github.com/gavinkhung/perceptron)
- Principal Component Analysis Repo (https://github.com/gavinkhung/pca)
- K Means Repo (https://github.com/gavinkhung/k-means-clustering/)
- Gradient Descent Repo (https://github.com/gavinkhung/gradient-descent)
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 (https://jupyterbook.org/en/stable/intro.html).
Usage
Step 1: Download the Jupyter Notebooks
sh chmod +x ./download_notebooks.sh ./download_notebooks.sh
Step 2: Building the Jupyter Book
Option 1: jupyter-book CLI
sh pip install -U jupyter-book jupyter-book build .
Option 2: Docker Compose
sh docker compose run jupyter-book docker compose down --volumes --rmi local
Option 3: Docker
``sh 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)
``sh 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/main.tex -o book/main.epub –mathml –embed-resources –standalone ``
Output
Marimo Interactive Notebooks
Marimo
Mathematically Explained
latex
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