deep learning education / textbook
Interactive deep learning book with Jupyter notebooks, math, and multi-framework code (MXNet, PyTorch, TensorFlow).
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose for an actively maintained, applied ML/DL book with Scikit-Learn/Keras/TensorFlow.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose for a production/MLOps-focused curriculum rather than deep learning theory.
Learn how to develop, deploy and iterate on production-grade ML applications.
Choose for a math/computing-heavy theory compendium with intuition rather than code-first notebooks.
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
Choose if you prefer fastai's high-level API and practical focus, though code base is stale.
The fastai book, published as Jupyter Notebooks
Choose for a structured course with assignments and leaderboards rather than a textbook.
Open Machine Learning Course
Choose for a project-based ML engineering bootcamp with hands-on homework.
Learn ML engineering for free in 4 months! Register here 👇🏼
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Older static course with Python examples, useful for a snapshot curriculum.
:speech_balloon: Machine Learning Course with Python:
Old edition of the Hands-On ML book, superseded by handson-ml3.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
Choose for from-scratch implementations of classic ML algorithms, narrower than deep learning.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Choose if your focus is exclusively LLMs rather than general deep learning.
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
These projects were analysed and named d2l-en among their alternatives. The relationship is not symmetric — how d2l-en rates them is a separate judgement, made when d2l-en is analysed in its own right.
calls d2l-en “Choose this for a rigorous interactive deep-learning textbook with multi-framework code and exercises rather than a translated notes collection.”
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
calls d2l-en “Choose if you want a deep, interactive textbook with code and math instead of a roadmap.”
Roadmap to becoming an Artificial Intelligence Expert in 2022
calls d2l-en “choose for the official English multi-framework D2L book (PyTorch/TF/JAX) rather than the Chinese PyTorch-only adaptation”
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
calls d2l-en “English interactive deep learning book with multi-framework code; a solid general alternative to the TF2-specific book.”
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
calls d2l-en “Interactive DL book with multi-framework code; choose for a deep-learning-only rigorous text.”
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 d2l-en “when you need an English interactive DL book; it is more comprehensive but not actively maintained.”
✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】
calls d2l-en “choose it for a comprehensive multi-framework deep learning textbook codebase, though not updated recently.”
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
calls d2l-en “Use it for a rigorous math+coding interactive deep-learning textbook, but it is deep-learning-only and last updated about two years ago.”
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
calls d2l-en “Use for an interactive deep-learning textbook with academic depth, but it has not been pushed in over 18 months.”
:speech_balloon: Machine Learning Course with Python: