machine learning education / tutorials
A collection of machine learning, deep learning, NLP, and reinforcement learning examples and tutorials, organized as code for a set of courses, using Python and multiple frameworks.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
When you want a broad, actively-maintained collection of ML/DL/NLP tutorials and interview-oriented code, a strong drop-in peer for the seed.
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。
When you want a structured open ML course with articles and notebooks, a maintained and more guided alternative to the seed.
Open Machine Learning Course
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
When you want structured deep learning labs from MIT, better for a semester-style course than the seed's mixed-topic examples.
Lab Materials for MIT 6.S191: Introduction to Deep Learning
When you want a comprehensive, well-structured deep learning tutorial based on Hung-yi Lee's course, rather than the seed's multi-topic course fragments.
《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
When you want academic deep learning notebooks (PyTorch) from a university course, better for a formal DL curriculum.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
When you want to understand ML algorithms by implementing them with NumPy and interactive demos, not just using high-level libraries.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
When you want clean, minimal implementations of common ML algorithms without the extra tutorial fluff.
Minimal and clean examples of machine learning algorithms implementations
When you want a university Keras deep learning course with structured assignments, instead of the seed's less organized snippets.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
When you want ML algorithms derived from first principles and visualized in notebooks, great for intuition.
ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy
When you want video-based deep learning experiments and notes, a more informal complement to the seed.
Videos, notes and experiments to understand deep learning
When you want to study and implement state-of-the-art deep learning papers with detailed annotations, more advanced than the seed's basics.
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
When you want the code companion to the classic 'Python Machine Learning' book, good for a structured book-based approach.
The "Python Machine Learning (1st edition)" book code repository and info resource
When you want a Chinese version of the practicalAI course, which teaches ML with PyTorch in a project-based way.
AI实战-practicalAI 中文版
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
When you want a fastai-focused deep learning book in notebooks, but note it hasn't been updated in over 18 months.
The fastai book, published as Jupyter Notebooks
When you want a Chinese-language PyTorch tutorial series, but it's older and unmaintained.
Build your neural network easy and fast, 莫烦Python中文教学
When you want classic TensorFlow examples covering v1 and v2, but it's older and not actively maintained.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
When you want a Chinese-language PyTorch handbook, but it's older and not updated recently.
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
When you want simple TensorFlow tutorials from an older course, good for beginners but stale.
:satellite: Simple and ready-to-use tutorials for TensorFlow
When you want a Chinese-language TensorFlow tutorial series, but it's very old (5.8 years since last push).
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
When you want bare-bones NumPy implementations of ML algorithms from a classic, but unmaintained repo.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
When you want a popular PyTorch tutorial for researchers, but it's older and not recently updated.
PyTorch Tutorial for Deep Learning Researchers
When you want GPL-licensed implementations of many ML models in NumPy, but it's older and unmaintained.
Machine learning, in numpy
When you want a collection of Keras GAN implementations, but it's old (3.7 years) and specific to GANs.
Keras implementations of Generative Adversarial Networks.
When you want a broad collection of ML/DL tutorials, but it hasn't been updated in two years.
A resource for learning about Machine learning & Deep Learning
When you want code recipes from the TensorFlow Machine Learning Cookbook, but it's stale (2.3 years since last push).
Code for Tensorflow Machine Learning Cookbook
These projects were analysed and named machine_learning_examples among their alternatives. The relationship is not symmetric — how machine_learning_examples rates them is a separate judgement, made when machine_learning_examples is analysed in its own right.
calls machine_learning_examples “choose it for a broad collection of ML and DL examples across many frameworks rather than paper-specific notes.”
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
calls machine_learning_examples “Choose for a broad, independent set of ML examples across many libraries and topics if you want more than TensorFlow.”
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
calls machine_learning_examples “Choose for a broad set of ML examples with practical code, not purely from-scratch.”
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
calls machine_learning_examples “choose when you want a broad collection of tutorial-style examples from an instructor's courses.”
Minimal and clean examples of machine learning algorithms implementations
calls machine_learning_examples “choose for a grab-bag of standalone ML/DL code examples across many topics instead of a single course.”
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
calls machine_learning_examples “Broader ML example collection covering many libraries; pick when you need examples beyond TensorFlow deep learning.”
TensorFlow Tutorials with YouTube Videos
calls machine_learning_examples “A collection of ML examples, narrower than the seed's all-encompassing interview guide.”
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。
calls machine_learning_examples “Choose when you want standalone examples for individual ML/DL topics rather than a coherent course sequence.”
Lab Materials for MIT 6.S191: Introduction to Deep Learning
calls machine_learning_examples “Scattered classical-ML and DL example scripts; pick for a wide pile of small examples rather than a structured curriculum.”
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.