The best books about applied deep learning

Who am I?

I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.

I wrote...

GANs in Action: Deep Learning with Generative Adversarial Networks

By Jakub Langr, Vladimir Bok,

Book cover of GANs in Action: Deep Learning with Generative Adversarial Networks

What is my book about?

GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.

Generative Adversarial Networks, GANs, are an incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the "real thing." By pitting two neural networks against each other—one to generate fakes and one to spot them—GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes, GANs are a huge step forward in deep learning systems.

The books I picked & why

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Deep Learning with Python

By Francois Chollet,

Book cover of Deep Learning with Python

Why this book?

This is a fantastic book to get you started. It is written by the author of a leading deep learning framework Keras, which makes even Tensorflow very easy to use. Chollet is a true leader of the deep learning craft and the Manning team always does an excellent job of forcing authors to make the subject matter accessible. Highly recommended!

Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

By Jeremy Howard, Sylvain Gugger,

Book cover of Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

Why this book?

Jeremy Howard is the lead author and has always been a world-class educator. This book is based on his course, which has managed to splice all rigor, simplicity, and cutting edge techniques into one course. It also uses its custom framework built on PyTorch, which is the dominant language for researchers. This book is very practically oriented and gets you off the ground very quickly with your own projects!

How to Measure Anything: Finding the Value of Intangibles in Business

By Douglas W. Hubbard,

Book cover of How to Measure Anything: Finding the Value of Intangibles in Business

Why this book?

While technically not about deep learning, this book is fantastic for those interested in pursuing applied or practical machine learning problems. While the central thesis of a topic can be reduced to “Frequently, models are valuable simply by reducing uncertainty,” it is definitely worth a read as there’s a lot of deep thinking in this book!

Deep Learning

By Ian Goodfellow, Yoshua Bengio, Aaron Courville

Book cover of Deep Learning

Why this book?

This is a very technical, truly academic book. I’d recommend reading it 4th or 5th if you felt that prior books did not have sufficient rigour. While this book is academic, it has been lauded by many for its clarity. This is a great book to really think about fundamentals and see what sorts of things can go wrong—even in applied settings.

Grokking Deep Learning

By Andrew W. Trask,

Book cover of Grokking Deep Learning

Why this book?

This book is a fantastic intro to someone who really wants to intuitively understand deep learning. It can help you clear up things where you are stuck or simply if you’re having trouble explaining parts of your algorithm to your business stakeholders. It is also a really good preparation if you want a really solid, practical basis to come up with new tweaks or types of models.

5 book lists we think you will like!

Interested in machine learning, deep learning, and python?

5,716 authors have recommended their favorite books and what they love about them. Browse their picks for the best books about machine learning, deep learning, and python.

Machine Learning Explore 31 books about machine learning
Deep Learning Explore 10 books about deep learning
Python Explore 25 books about python

And, 3 books we think you will enjoy!

We think you will like Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, Practical Natural Language Processing, and The Hundred-Page Machine Learning Book if you like this list.