Editor’s Disclaimer: The links on this page contain my Amazon Associate referral code. By clicking on any of the Amazon links below, I will earn a small commission from qualifying sales. The commissions will allow me to further develop new content.
With the disclaimer out of the way, here are a few books I’ve enjoyed reading over the years. Whenever possible, I aim to read on my Amazon Kindle Paperwhite as books are discounted and I can have more than one book with me!
Deep Learning Textbooks
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016)
- Theory heavy textbook covering deep learning advances.
- Available for free at: https://www.deeplearningbook.org/
- (Free) Dive into Deep Learning by Aston Zhang, Zachary Lipton, Mu Li, and Alexander Smola (2021)
- Grokking Deep Learning, First Edition by Andrew Trask (2019)
- Deep Learning with Python, Second Edition by François Chollet (2021) and
Deep Learning with R, First Edition by François Chollet and J.J. Allaire (2018)
- These texts describe how to use Keras to train deep learning networks in Python and R. The latter is slightly out of date.
- Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools by Eli Stevens, Luca Antiga, Thomas Viehmann (2020)
- Unlike the previous texts, this one focuses on using the PyTorch framework for deep learning.
- Available for free at: https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD, First Edition by Jeremy Howard and Sylvain Gugger (2020)
- Book chapters are available for free in Jupyter Notebook form at: https://github.com/fastai/fastbook
Python Data Science Textbooks
- Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas (2016)
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Second Edition by Wes McKinney (2017)
R Data Science Textbooks
- Hands-On Programming with R: Write Your Own Functions and Simulations by Garrett Grolemund (2014)
- Available for free at: https://rstudio-education.github.io/hopr/
- R for Everyone: Advanced Analytics and Graphics, Second Edition by Jared Lander (2017)
- Practical Data Science with R, Second Edition by Nina Zumel and John Mount (2019)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund (2016)
- Available for free at: https://r4ds.had.co.nz/
- Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R, First Edition by Hadley Wickham (2021)
- Available for free at: https://mastering-shiny.org/
- Advanced R, Second Edition by Hadley Wickham (2019)
- Available for free at: https://adv-r.hadley.nz/
- R Packages: Organize, Test, Document, and Share Your Code by Hadley Wickham (2015)
- Available for free at: https://r-pkgs.org/
- ggplot2: Elegant Graphics for Data Analysis (Use R), Second Edition by Hadley Wickham (2016)
- Available for free at: https://ggplot2-book.org/
- Note, a third edition was submitted to a publisher, but has yet to appear on Amazon.
High Performance Computing
- Parallel Computing for Data Science: With Examples in R, C++ and CUDA, First Edition by Norman Matloff (2015)