deep learning textbook / educational notebooks
A community PyTorch adaptation of the Dive Into Deep Learning book, with Jupyter notebooks reproducing each chapter's MXNet code in PyTorch.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Model-by-model notebooks spanning PyTorch, TF and Keras; choose it to look up a specific architecture instead of following a book linearly.
A collection of various deep learning architectures, models, and tips
Short self-contained PyTorch tutorial scripts; choose for quick API examples instead of a full textbook progression.
PyTorch Tutorial for Deep Learning Researchers
MIT 6.S191 lab notebooks in TensorFlow; pick it for a compact course-style TensorFlow curriculum.
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Companion notebooks for a TensorFlow machine-learning book; choose it if your stack is TensorFlow, not PyTorch.
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Beginner TF1/TF2 example scripts; choose it for the TensorFlow counterparts of the D2L recipes.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
The very same MXNet-to-PyTorch port with a larger community, but untouched for ~5 years, so only worth picking for older PyTorch-era notebooks.
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。
One learner's PyCharm implementations of the same course, narrower and stale; choose it for condensed Chinese study notes rather than a full chapter port.
✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。
Lecture notes for the same D2L course in Chinese; pick for skimming theory, not for runnable notebooks.
Notes about courses Dive into Deep Learning by Mu Li
Companion code for a different, computer-vision-only Chinese textbook, unmaintained for ~6 years; only useful if your course uses that book.
《深度学习与计算机视觉》配套代码
Scattered classical-ML and DL example scripts; pick for a wide pile of small examples rather than a structured curriculum.
A collection of machine learning examples and tutorials.
Bare NumPy implementations of classical algorithms; choose it to see internals, not for PyTorch idioms.
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.
A single-topic AWS NLP course in notebooks; pick it when you need NLP-specific lessons rather than D2L's breadth.
Machine Learning University: Accelerated Natural Language Processing Class
Companion code for a scikit-learn-centred ML book; choose it for classical ML workflow rather than deep learning.
The "Python Machine Learning (1st edition)" book code repository and info resource
A grab-bag of data-science notebooks pinned to old frameworks; useful only for scattered, dated examples.
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.
An AWS tabular-data course; narrower than D2L, pick it for feature engineering on tabular datasets.
Machine Learning University: Accelerated Tabular Data Class
These projects were analysed and named d2l-pytorch among their alternatives. The relationship is not symmetric — how d2l-pytorch rates them is a separate judgement, made when d2l-pytorch is analysed in its own right.
calls d2l-pytorch “stale PyTorch adaptation of the same D2L book; only useful if you want that older snapshot”
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。