TensorFlow tutorials / ML education
TensorFlow-Examples provides beginner-friendly code examples and tutorials for TensorFlow, covering basic operations and machine learning models in both TF v1 and v2.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose for a framework-free, minimal Python implementation of classic ML algorithms instead of TensorFlow-based examples.
Minimal and clean examples of machine learning algorithms implementations
Choose for a broader, multi-framework collection covering PyTorch and TensorFlow if you need beyond just TensorFlow.
A resource for learning about Machine learning & Deep Learning
Choose for a structured, PyTorch-based ML curriculum in Chinese rather than TensorFlow examples.
AI实战-practicalAI 中文版
Choose when you want first-principles derivations and visualizations in NumPy instead of TensorFlow-specific code.
ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy
Choose for a comprehensive structured course with assignments and classical ML (scikit-learn) instead of a bare example collection.
Open Machine Learning Course
Choose for video-based guided learning with scikit-learn instead of static TensorFlow examples.
Jupyter notebooks from the scikit-learn video series
Choose for a comprehensive, book-led ML/DL course with PyTorch and Hugging Face if you prefer a textbook approach.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
Choose to learn by building complete mini-projects rather than isolated examples if you prefer project-based work.
This repository contains mini projects in machine learning with notebook files
Choose for a top-down, fastai/PyTorch approach to deep learning with a complete book if you prefer PyTorch.
The fastai book, published as Jupyter Notebooks
Choose for a PyTorch-specific structured handbook in Chinese rather than TensorFlow examples.
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
Choose for an up-to-date, Chinese-language deep learning course with PyTorch, including theory and code.
《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 a broad, independent set of ML examples across many libraries and topics if you want more than TensorFlow.
A collection of machine learning examples and tutorials.
Choose for a university-quality, lab-based deep learning course with TensorFlow/Keras if you want homework-style assignments.
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Choose for a straightforward PyTorch tutorial in Chinese, though it is older and less maintained.
Build your neural network easy and fast, 莫烦Python中文教学
Choose for an applied, Keras-focused deep learning course with practical assignments if you want real-world applications.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose for a simple, day-by-day challenge format, but it is outdated and not TensorFlow-based.
100 Days of ML Coding
Choose for extremely simple pure-Python implementations of basic ML algorithms, though unmaintained.
Plain python implementations of basic machine learning algorithms
Choose for deeper coverage of pattern classification methods with more explanation, but it is older and stale.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Choose for a full NumPy reimplementation of ML algorithms, but it is inactive and not TensorFlow.
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.
Choose for a thorough, self-contained NumPy implementation of ML algorithms, but it is inactive for years.
Machine learning, in numpy
Choose for simple TensorFlow tutorials if you specifically need TF1-style code, but it is outdated.
:satellite: Simple and ready-to-use tutorials for TensorFlow
Choose for the classic Python ML book code if you prefer the older scikit-learn/TF1 approach; not current.
The "Python Machine Learning (1st edition)" book code repository and info resource
Choose for a dedicated Keras GAN example collection if your focus is generative models, not general TensorFlow.
Keras implementations of Generative Adversarial Networks.
Choose for a very old Chinese TensorFlow tutorial, but it is TF1 and unmaintained.
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
These projects were analysed and named TensorFlow-Examples among their alternatives. The relationship is not symmetric — how TensorFlow-Examples rates them is a separate judgement, made when TensorFlow-Examples is analysed in its own right.
calls TensorFlow-Examples “choose it for a large set of TensorFlow examples for beginners rather than paper-specific implementations.”
🧑🏫 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 TensorFlow-Examples “choose for TensorFlow-based, runnable ML/DL examples rather than a scikit-learn 100-day challenge.”
100 Days of ML Coding
calls TensorFlow-Examples “Choose when you prefer learning via TensorFlow examples rather than NumPy 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 TensorFlow-Examples “Choose for TensorFlow-specific tutorials rather than general from-scratch algorithms.”
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
calls TensorFlow-Examples “choose when you want TensorFlow example snippets rather than PyTorch tutorials.”
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
calls TensorFlow-Examples “Choose if you want TensorFlow-focused examples instead of the seed's scikit-learn/Theano code.”
The "Python Machine Learning (1st edition)" book code repository and info resource
calls TensorFlow-Examples “Choose it if you want simple TensorFlow code examples instead of PyTorch.”
Build your neural network easy and fast, 莫烦Python中文教学
calls TensorFlow-Examples “Beginner TF1/TF2 example scripts; choose it for the TensorFlow counterparts of the D2L recipes.”
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
calls TensorFlow-Examples “Older TensorFlow-only example notebooks; choose for TF-specific snippets, not a broad DS survey.”
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
calls TensorFlow-Examples “choose for quick TensorFlow code samples; it is a narrow example collection, not a full textbook.”
邱锡鹏《神经网络与深度学习》第二版与通识版：电子书、章节目录、学习资源与勘误。
calls TensorFlow-Examples “TensorFlow-specific examples, narrower than ML-NLP and unmaintained for over two years.”
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 TensorFlow-Examples “Use only for concise TensorFlow code snippets; it is framework-specific and outdated.”
《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
calls TensorFlow-Examples “Use for a compact, example-focused TensorFlow reference (v1 and v2) instead of a full course, though unmaintained.”
:satellite: Simple and ready-to-use tutorials for TensorFlow
calls TensorFlow-Examples “Older but familiar TensorFlow examples for legacy codebases; unmaintained.”
TensorFlow Tutorials with YouTube Videos
calls TensorFlow-Examples “When you want classic TensorFlow examples covering v1 and v2, but it's older and not actively maintained.”
A collection of machine learning examples and tutorials.
calls TensorFlow-Examples “Choose when you want simple TensorFlow example snippets rather than MIT's conceptual labs.”
Lab Materials for MIT 6.S191: Introduction to Deep Learning
calls TensorFlow-Examples “For legacy TensorFlow 1.x examples and a simpler, older presentation.”
A resource for learning about Machine learning & Deep Learning
calls TensorFlow-Examples “When you specifically need legacy TensorFlow v1/v2 example snippets for comparison or historical reference.”
AI实战-practicalAI 中文版