Textbook & Reference Book
Machine Learning
Free Machine Learning Books, GitHub 2018
Mathematics for Machine Learning, by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Cambridge University Press. 2020. (Free PDF)
Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David, Published by Cambridge University Press, 2014. (Free PDF)
Introduction to Machine Learning, 3rd, E. Alpaydin, MIT Press, 2014.
Machine Learning in Action. Peter Harrington, Manning Publications Co., 2012.
Machine Learning: An Algorithmic Perspective, S. Marsland, Chapman and Hall/CRC, 2009.
Machine Learning with Python
Machine Learning with Python Cookbook 2nd, by Kyle Gallatin and Chris Albon, O'Reilly, 2023 (Scikit-learn, PyTorch)
Machine Learning with Python - Theory and Implementation, Amin Zollanvari, Springer, 2023. (Scikit-learn, Tensorflow, Keras)
Statistics and Machine Learning in Python, R0.5, by Edouard Duchesnay, Tommy Lofstedt, Feki Younes; Engineering school. France. 2021. hal-03038776v3 (Scikit-learn) (Free PDF)
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition, Sebastian Raschka, Vahid Mirjalili; Packt, 2019. 3rd code@GitHub (Scikit-learn, Tensorflow)
Python機器學習第三版（上），Sebastian Raschka, Vahid Mirjalili／劉立民, 吳建華，博碩，2020
Hands on Machine Learning with Scikit Learn Keras and TensorFlow, 2nd Edition, Aurélien Géron, O’Reilly, 2019. (GitHub Jupyter) (Scikit-learn, Tensorflow, Keras)
Machine Learning with Python, TutorialPoint, 2019 (Free PDF)
Introduction to Machine Learning with Python - A Guide for Data Scientists, by Andreas C. Müller and Sarah Guido, OReilly, 2017. GitHub
最好懂的機器學習書：使用Python了解原理、演算法及實戰案例，劉艷, 韓龍哲, 李沫沫，深智數位，2023
機器學習：彩色圖解+基礎微積分+Python實作 王者歸來(第三版)，洪錦魁，深智數位，2021
Deep Learning
Dive into Deep Learning, by Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola. Cambridge University Press; 1st edition, 2023 (Free PDF @ https://d2l.ai)
Deep Learning - Foundations and Concepts, by Christopher M. Bishop and Hugh Bishop, Springer, 2024 (Free Download from Springer Link)
Understanding Deep Learning, by Simon J.D. Prince, MIT Press, 2023 (Free download from GitHub)
Deep Learning with Python, 2nd, by Nikhil Ketkar Jojo Moolayil, APress, 2021
Deep Learning with Python, by François Chollet, Manning, 2018 (Keras)
中文版：Python深度學習，中國工信／人民郵電，2018
Deep Learning. By Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 (Free Online HTML) (輔大圖書館)(免費中文版PDF)
本書作者 Ian Goodfellow 為GAN模型的創始者，並曾在 Google Brain 和 OpenAI 工作。不少人認為這本書是深度學習領域的聖經，因為它是迄今為止唯一一本融合了前幾十年研究工作的鴻篇巨著。
本書偏重理論，書中不僅有大量的公式，同時寫得比較枯燥乾澀。除非自認數學基礎深厚，否則不建議從本書入門。本書比較適合那些經過幾年相關工作後，仍想進一步掌握深度學習的從業者。對於擁有較多專業領域知識且正準備初次踏入 AI 行業的專業程序員而言，這也是一本比較全面的指南。
Deep Learning: A Practitioner’s Approach. By Adam Gibson and Josh Patterson, O'Reilly Media, 2017
該書主要使用 Java 的深度學習框架 DL4J。目前 AI 領域的研究大多數使用 Python 語言實現，不過隨著越來越多企業湧入機器學習領域，Java 的使用可能會逐漸增多。由於 Java 擁有龐大的生態系統，現在的大公司裡，它仍然是主要的開發工具。
本書的讀者設定是深度學習的初學者。因此，如果你已經有一些深度學習的基本知識、並想進一步深入研究如何用 Java 實現深度學習的話，請直接跳過前面的例子。但是如果你沒什麼深度學習經驗，Java 也不太熟悉的話，那麼這本書值得你細細研讀。 尤其是第 4 章「出色的深度學習架構」，提供了一個可以幫你解決現實應用中架構問題的關鍵方法 。
L. Deng, D. Yu. “Deep learning: methods and applications.” Foundations and Trends in Signal Processing, NOW Publishers, 7.3–4, 197-387, 2014 (Free PDF)
Yoshua Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, 2(1), pp.1-127, 2009 (Free PDF)