Best books for deep learning

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In this data-driven era, machines are learning to figure things out like humans do, by mimicking the functionality of the human brain and leading towards the creation of AI (Artificial Intelligence). At the core of this lies deep learning. Deep Learning is an interesting subset of Machine Learning, in this article we will dive deep into the best books for learning Deep Learning.

1. Deep Learning with Python by François Chollet

This book focuses on the practical learning approach (If you want theoretical learning approach then checkout other books below). First edition of this book was published in November 2017, and the second edition, which is fully color printed, was published in October 2021. It is one of the best books for deep learning.

Author

The author of Deep Learning with Python, François Chollet, is a software engineer in Google and created Keras deep-learning library and also contributed in TensorFlow machine learning framework. Directly learning from the creator of Keras himself is one of the great opportunities.

Requirements

Readers need to have intermediate knowledge of Python to get full benefit from the book, no prior knowledge of Keras, TensorFlow or machine learning is required.

2. Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal

This books provides in-depth information about neural networks and deep learning. It focuses on theoretical approach more then practical approach. It explores applications in recommender systems, image classification, text analytics, and reinforcement learning-based gaming. Second edition of it have significant updates compared to the first one, it includes materials on graph neural networks, attention mechanisms, transformers, adversarial learning, and large language models.

Author

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and earned his Ph.D. from the Massachusetts Institute of Technology in 19961. His research contributions span various areas, including data mining, machine learning, and deep learning.

Requirements

Prior knowledge in linear algebra, calculus, supervised learning, feedforward neural networks, backpropagation, and Python programming is beneficial for understanding the material.

3. Deep Learning: A Visual Approach by Andrew Glassner

Deep Learning: A Visual Approach by Andrew Glassner is one of the best books for deep learning for beginners, It is designed to serve a wide range of audience, including programmers, artists, scientists, engineers, musicians, and anyone interested in deep learning. It’s clear visual representation, easy writing style make it more beginner friendly. It is one of the best books for deep learning for beginners.

Author

Andrew S. Glassner, an expert in computer graphics, has made significant contributions to the field. He is known for editing the Graphics Gems series and authoring “An Introduction to Ray Tracing”. His work extends beyond graphics to include interactive fiction, writing, directing, and consulting in gaming and online entertainment. Currently, Glassner integrates deep learning with visual effects production at Weta Digital. His multifaceted expertise makes him a valuable figure in technology and creativity.

Requirements

Readers only need to have basic athematic skills and beginner level programming skills to get started, which make it one of the best beginner friendly books for deep learning.

4. Deep Learning for Coders with Fastai & PyTorch by Jeremy Howard and Sylvain Gugger

It is a highly regarded and one of the best books for deep learning. This book is known for its practical approach to learn deep learning. It uses Fastai library and PyTorch, which are powerful tools in the field of deep learning. This book covers various topics including computer vision, natural language processing, tabular data, and collaborative filtering. Collaboration of Jeremy Howard and Sylvain Gugger makes this book one of the best books for deep learning.

Author

Jeremy Howard has decades of experience in neural networks and teaching. His journey with neural networks began 25 years ago, and he has been using and teaching machine learning for nearly 30 years. He is one of the co-creator of the fastai library. He has expertise various domains, including computer vision, natural language processing, and tabular data analysis.

Sylvain Gugger, the other co-author, is equally influential. His work alongside Jeremy has transformed fastai into a powerful tool for learners. Sylvain’s background in Python programming and deep understanding of the PyTorch library have been instrumental.

Requirements

Familiarity with Python is essential, as both fastai and PyTorch are Python-based libraries. Basic knowledge of linear algebra (matrices, vectors) and calculus (derivatives, gradients) is valuable for understanding deep learning algorithms.

5. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster

It is one of the best books for deep learning, which explores the concepts of generative modeling using deep learning techniques. The book covers various generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), encoder-decoder models, and world models. You’ll learn how to create these models from scratch using popular deep learning libraries like TensorFlow and Keras. The book provides insights on making your models learn more efficiently and become more creative.

Author

David Foster is an expert in the field of data science and machine learning. He co-founded Applied Data Science Partners, a data science consultancy based in London, where he leads projects that apply advanced machine learning techniques to solve complex business problems. Foster holds a PhD in Physics from the University of Cambridge and has a strong background in quantitative research and analytics. His work bridges the gap between theoretical research and practical, real-world applications, making him a respected figure in the data science community.

Requirements

Readers should have a solid understanding of machine learning basics, proficiency in Python (especially with libraries like NumPy and pandas), and knowledge of deep learning fundamentals, including neural networks and frameworks like TensorFlow or PyTorch. Additionally, a strong grasp of linear algebra, calculus, probability and statistics.

FAQs

Deep Learning with Python by François Chollet

Readers need to have intermediate knowledge of Python to get full benefit from the book

Editor's Rating:
4.7

Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal

Prior knowledge in linear algebra, calculus, supervised learning, feedforward neural networks, backpropagation, and Python programming is beneficial for understanding the material.

Editor's Rating:
4.4

Deep Learning: A Visual Approach by Andrew Glassner

Readers only need to have basic athematic skills and beginner level programming skills to get started

Editor's Rating:
4.7

Deep Learning for Coders with Fastai & PyTorch by Jeremy Howard and Sylvain Gugger

Familiarity with Python is essential, as both fastai and PyTorch are Python-based libraries.

Editor's Rating:
4.7

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster

Readers should have a solid understanding of machine learning basics, proficiency in Python (especially with libraries like NumPy and pandas)

Editor's Rating:
4.6