deep learning education / course notebooks
MIT 6.S191 Introduction to Deep Learning lab materials: Jupyter notebooks with coding exercises for learning deep learning.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose when you want a current university deep-learning course with PyTorch notebooks and more in-depth exercises than MIT 6.S191.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
Choose when you want a Keras-focused applied deep learning course with self-contained notebooks instead of MIT 6.S191.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose when you want a project-based fastai/PyTorch book narrative rather than MIT's lecture-aligned labs.
The fastai book, published as Jupyter Notebooks
Choose when you want Chinese-language notes following Hung-yi Lee's DL lectures rather than MIT labs.
《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 when you want a Chinese-translated practical ML curriculum with end-to-end projects rather than MIT's lecture-aligned labs.
AI实战-practicalAI 中文版
Choose when you want a complete interactive textbook with code and math in Chinese rather than MIT lab notebooks.
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
Choose when you need a theoretical Chinese deep learning textbook rather than hands-on labs.
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。
Choose when you want a PyTorch-first video course with project notebooks rather than MIT's model-focused labs.
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Choose when you want a broad video-backed ML/DL tutorial collection rather than MIT's focused course.
A resource for learning about Machine learning & Deep Learning
Choose when you want a notebook-based book covering Scikit-Learn, PyTorch, and Hugging Face rather than MIT's labs.
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 when you want a broader beginner AI curriculum with 12 weeks of lessons rather than MIT's deep-learning-intensive labs.
12 Weeks, 24 Lessons, AI for All!
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose when you want standalone examples for individual ML/DL topics rather than a coherent course sequence.
A collection of machine learning examples and tutorials.
Choose when you want minimal Chinese PyTorch examples for quick neural-network demos rather than MIT's full course.
Build your neural network easy and fast, 莫烦Python中文教学
Choose when you want simple TensorFlow example snippets rather than MIT's conceptual labs.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Choose when you want the classic Raschka book code for ML/DL rather than MIT's current course.
The "Python Machine Learning (1st edition)" book code repository and info resource
Choose when you want informal experiment notebooks and videos for DL intuition rather than a structured course.
Videos, notes and experiments to understand deep learning
Choose when you want a beginner PyTorch handbook in Chinese rather than MIT's full DL course; it is older and framework-specific.
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 when you want simple ready-to-use TensorFlow tutorials, though they are older and not a full DL course.
:satellite: Simple and ready-to-use tutorials for TensorFlow
Choose when you want legacy Chinese TensorFlow 1.x tutorials rather than MIT's current labs.
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
These projects were analysed and named introtodeeplearning among their alternatives. The relationship is not symmetric — how introtodeeplearning rates them is a separate judgement, made when introtodeeplearning is analysed in its own right.
calls introtodeeplearning “Use for an active, MIT-lab-style structured TensorFlow/Keras deep learning course with exercises.”
:satellite: Simple and ready-to-use tutorials for TensorFlow
calls introtodeeplearning “choose for the active MIT 6.S191 lab materials with official assignments and Colab support.”
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
calls introtodeeplearning “For a lab-based MIT course on deep learning.”
A resource for learning about Machine learning & Deep Learning
calls introtodeeplearning “choose it for a university-course lab structure with hands-on assignments instead of paper 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 introtodeeplearning “Choose for a university-quality, lab-based deep learning course with TensorFlow/Keras if you want homework-style assignments.”
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
calls introtodeeplearning “Choose for a structured deep-learning lab course with 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.
calls introtodeeplearning “choose if you want a current university course with TensorFlow/Keras exercises instead of fastai.”
The fastai book, published as Jupyter Notebooks
calls introtodeeplearning “choose when you want a rigorous MIT-style deep learning course with TensorFlow labs.”
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 introtodeeplearning “choose for MIT-style lecture labs with hands-on exercises, not a comprehensive theory text.”
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 introtodeeplearning “Choose for a well-maintained, TensorFlow-based intro to deep learning from MIT.”
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
calls introtodeeplearning “MIT course labs with a structured curriculum, different approach (course/lab) focused on deep learning.”
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 introtodeeplearning “Choose for hands-on labs and video lectures, but it is a course supplement rather than a self-contained textbook.”
《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 introtodeeplearning “Interactive MIT 6.S191 lab exercises, choose if you want hands-on coding instead of the full theory book.”
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
calls introtodeeplearning “When you want structured deep learning labs from MIT, better for a semester-style course than the seed's mixed-topic examples.”
A collection of machine learning examples and tutorials.
calls introtodeeplearning “Choose it if you want TensorFlow-based lab assignments from MIT's deep learning course.”
Build your neural network easy and fast, 莫烦Python中文教学
calls introtodeeplearning “MIT 6.S191 lab notebooks in TensorFlow; pick it for a compact course-style TensorFlow curriculum.”
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
calls introtodeeplearning “When you want MIT's deep-learning lab exercises, but note it covers only deep learning.”
The "Python Machine Learning (2nd edition)" book code repository and info resource
calls introtodeeplearning “When you want MIT's TensorFlow deep learning lab notebooks and your scope is deep learning only.”
AI实战-practicalAI 中文版