Best Books For Deep Learning In Python

1.Deep Learning: A Visual Approach

Deep learning is a rapidly growing field in artificial intelligence (AI) that uses patterns hidden in data to understand and respond intelligently to human activities. It has been used in various applications, such as voice commands on phones, automotive systems to navigate road hazards, and online platforms to deliver personalized suggestions for movies and books. This accessible, highly-illustrated introduction to deep learning offers visual and conceptual explanations instead of equations, allowing readers to learn how to use key deep learning algorithms without the need for complex math. The book’s conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain key concepts in deep learning, including text generators, deep learning systems learning to play and win human games, image classification systems identifying objects or people in photos, and using machine learning techniques that form the core of modern AI.

Rated 5 on Goodreads.

You can buy this book here.

2.Deep Learning from Scratch: Building with Python from First Principles

Deep learning has become crucial for machine learning practitioners and software engineers due to the resurgence of neural networks. This book provides a comprehensive introduction to deep learning, starting with basic concepts and moving to advanced architectures. Author Seth Weidman teaches how to apply multilayer, convolutional, and recurrent neural networks from scratch. The book includes clear mental models, working code examples, mathematical explanations, methods for implementing multilayer neural networks, working implementations of convolutional and recurrent neural networks, and implementation using the popular PyTorch framework.

Rated 4.1 on Goodreads.

You can purchase this book here.

3.Practical Deep Learning: A Python-Based Introduction

Practical Deep Learning is a book that teaches beginners how to build datasets and models for training neural networks for their own deep learning (DL) projects. It focuses on the subfield of deep learning and provides a foundation for building models. The book covers topics like building a good training dataset, using scikit-learn and Keras libraries, and evaluating models’ performance. It covers classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines, as well as neural network training and convolutional neural networks. The book also includes experiments and a final case study that incorporates all learned knowledge. Practical Deep Learning is an excellent introduction to the dynamic and expanding field of machine learning.

Rated 4.7 on Amazon.

You can purchase this book on

4.Deep Learning with Python, Second Edition

Deep Learning with Python, Second Edition is a revised and expanded edition of the bestselling book, introducing deep learning using Python and Keras. The book covers topics such as image classification, time series forecasting, text classification, machine translation, text generation, neural style transfer, and image generation. The book is designed for both novice and experienced machine learning practitioners, offering practical techniques and theory for perfecting neural networks. The revised edition includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Deep learning is becoming essential knowledge for software developers, and modern tools like Keras and TensorFlow make it accessible to anyone, regardless of their background in mathematics or data science. The book provides intuitive explanations, crisp color illustrations, and clear examples to help readers develop the skills needed to develop deep-learning applications.

Rated 4.6 on Goodreads.

You can purchase this book on Amazon.

5.Neural Networks and Deep Learning: A Textbook

This book explores deep learning theory and algorithms, focusing on neural networks and their design concepts for various applications. It covers the basics of neural networks, such as the backpropagation algorithm, and discusses the connections between traditional machine learning models and neural networks. The book also covers training and regularization, radial-basis function (RBF) networks, and restricted Boltzmann machines. It then delves into advanced topics in neural networks, such as recurrent neural networks, convolutional neural networks, and graph neural networks. The book is written for graduate students and upper-level students, as well as researchers and practitioners working in the field. The second edition is reorganized and expanded, with separate chapters on backpropagation and graph neural networks. The focus is on modern deep learning ideas, such as attention mechanisms, transformers, and pre-trained language models. The book is written for graduate students and practitioners working within the field.

Rated 4.2 on Goodreads.

This book is available here.

6.Deep Learning: A Practitioner’s Approach, by Adam Gibson and Josh Patterson  

This guide by Adam Gibson and Josh Patterson provides practical information on machine learning and how it can make a difference in organizations. It introduces the open-source Deeplearning4j library for developing production-class workflows and provides real-world examples for training deep network architectures and running deep learning workflows on Spark and Hadoop. The guide covers machine learning concepts, deep learning, and its evolution from neural network fundamentals, as well as the fundamentals of tuning general neural networks and specific deep network architectures. It also teaches how to use DL4J natively on Spark and Hadoop.

Rated 3.8 on Goodreads.

7.Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition 

Deep Learning with TensorFlow and Keras is a book that teaches neural networks and deep learning techniques using TensorFlow (TF) and Keras. The book focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. It presents an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for cloud, mobile, and large production environments. The book covers how to create neural networks with TensorFlow, runs through popular algorithms, covers working example apps, and dives into TensorFlow in production, TF mobile, and TensorFlow with AutoML. Key features include learning how to use popular GNNs with TensorFlow for graph mining tasks, exploring transformers, applying self-supervised learning to natural language processing, computer vision, and audio signal processing, and combining probabilistic and deep learning models using TensorFlow Probability.

Rated 4.6 on Amazon.

This book can be purchased on Deep learning.

8.Deep Learning and the Game of Go

Deep Learning and the Game of Go is a book that teaches the application of deep learning to complex reasoning tasks by building a Go-playing AI. The book covers the foundations of machine and deep learning, using Python to build a bot and teach it the rules of the game. The ancient strategy game of Go is an incredible case study for AI, with a deep learning-based system defeating a world champion in 2016. The book introduces deep learning techniques by teaching readers to build a Go-winning bot and applying them to various scenarios. The book also covers enhancing classical game AI systems with deep learning and implementing neural networks for deep learning.

Rated 4 on Goodreads.

You can purchase this book on Amazon.

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