eriklindernoren/ML-From-Scratch Alternatives

Choose for minimal, clean NumPy implementations that are actively maintained.

Minimal and clean examples of machine learning algorithms implementations

Choose for interactive Jupyter demos with math explanations and a friendly from-scratch approach.

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Choose when you prefer learning via TensorFlow examples rather than NumPy from scratch.

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Choose when you want a high-level fastai-based educational book instead of NumPy internals.

The fastai book, published as Jupyter Notebooks

Choose for a Chinese-speaking deep-learning tutorial with PyTorch, not general NumPy ML.

《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases

Choose for ready-to-use TensorFlow tutorials instead of raw NumPy.

:satellite: Simple and ready-to-use tutorials for TensorFlow

Choose for a broad set of ML examples with practical code, not purely from-scratch.

A collection of machine learning examples and tutorials.

Choose for a structured deep-learning lab course with TensorFlow.

Lab Materials for MIT 6.S191: Introduction to Deep Learning

Choose for PyTorch-specific tutorials in Chinese/English, not NumPy from scratch.

Build your neural network easy and fast, 莫烦Python中文教学

Choose for TensorFlow tutorial series instead of NumPy from scratch.

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

Choose for an up-to-date academic deep-learning notebook suite using PyTorch.

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

Choose for videos and experiment notes on deep learning, not algorithm internals.

Videos, notes and experiments to understand deep learning

An interview-prep knowledge and code collection, not a focused from-scratch implementation.

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

Choose for a textbook-oriented approach using scikit-learn and TensorFlow, not NumPy from scratch.

The "Python Machine Learning (1st edition)" book code repository and info resource

Choose for a TensorFlow cookbook of practical recipes instead of NumPy implementations.

Code for Tensorflow Machine Learning Cookbook

Older but similar; choose if you accept GPL and don't need recent updates.

Machine learning, in numpy

Narrower: only GANs, and uses Keras rather than NumPy from scratch.

Keras implementations of Generative Adversarial Networks.