fastai/fastbook Alternatives

calls fastbook “Choose this English-only interactive book when you want a top-down, fastai-centered curriculum instead of D2L's math-first, framework-neutral approach.”

《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。

calls fastbook “choose it for a structured fastai-based book/course rather than paper-by-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 fastbook “Choose for a modern, practical deep learning book with code-first lessons and a strong focus on applications; more up-to-date than the seed's 2018 content.”

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

calls fastbook “Choose for a top-down, fastai/PyTorch approach to deep learning with a complete book if you prefer PyTorch.”

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

calls fastbook “Pick this if you want a fastai-centric applied deep-learning book with executable notebooks instead of a broad classical-ML Chinese compendium.”

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

calls fastbook “Choose this when you want a modern practical book with fastai and a top-down approach.”

吴恩达老师的机器学习课程个人笔记

calls fastbook “Choose when you want a high-level fastai-based educational book instead of NumPy internals.”

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 fastbook “Choose if you prefer fastai's high-level API and practical focus, though code base is stale.”

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

calls fastbook “choose when you want a fastai high-level API book with top-down teaching instead of PyTorch internals.”

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 fastbook “choose for a practical fastai-based deep-learning course with notebooks if you prefer high-level APIs over PyTorch-from-scratch”

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 fastbook “choose for a practical, high-level approach using fastai, if you prefer learn-by-building over theory.”

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 fastbook “Choose for a top-down deep learning course using fastai's high-level API instead of raw PyTorch.”

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

calls fastbook “A fastai-based book and notebook course for deep learning, a different pedagogical approach and library-focused.”

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 fastbook “Choose for a practical fastai-based introduction with runnable notebooks, but it is less theory-focused and stale (no push in over 18 months).”

《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 fastbook “Use for a comprehensive, modern deep learning book built on fastai/PyTorch rather than raw TensorFlow.”

:satellite: Simple and ready-to-use tutorials for TensorFlow

calls fastbook “Code-first deep learning book in Jupyter notebooks; choose for a practical, top-down learning path rather than a theory-first text.”

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

calls fastbook “When you prefer the fastai top-down teaching style.”

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.

calls fastbook “when you prefer a book/top-down fastai approach to deep learning instead of a broad classical ML course.”

Open Machine Learning Course

calls fastbook “choose for a book-driven fastai deep learning course with notebooks if you prefer the fastai pedagogy; fewer recent updates.”

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

calls fastbook “Choose when you want a project-based fastai/PyTorch book narrative rather than MIT's lecture-aligned labs.”

Lab Materials for MIT 6.S191: Introduction to Deep Learning