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Selection of the Best Deep Learning Books

Top Deep Learning books recommendations from industry practitioners.

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Deep Learning (Adaptive Computation and Machine Learning Series)

Goodreads Rating: 4.46/5
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep...
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Deep Learning with Python

Goodreads Rating: 4.66/5
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition.
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Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Goodreads Rating: -/5
A comprehensive guide to advanced deep learning techniques, including autoencoders, GANs, VAEs, and deep reinforcement learning that drive today's most impressive AI results.
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Deep Learning and the Game of Go

Goodreads Rating: 4/5
Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.
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Deep Learning: A Practitioner's Approach

Goodreads Rating: 3.76/5
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
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Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Goodreads Rating: 4.15/5
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music.
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Deep Learning: Engage the World Change the World

Goodreads Rating: 4/5
Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, whole-system change and transform learning for all students.
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Deep Learning from Scratch: Building with Python from First Principles

Goodreads Rating: 4.5/5
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.
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Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Goodreads Rating: 4.5/5
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
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Deep Learning (The MIT Press Essential Knowledge series)

Goodreads Rating: 4.35/5
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.
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Deep Learning With Python: Comprehensive Guide of Tips and Tricks using Deep Learning with Python Theories

Goodreads Rating: -/5
This book is designed to help you use Python for deep learning, including how to build and run deep learning models using Keras. This book also includes deep learning techniques, sample code, and technical content. The mathematical foundations of deep learning are subtle: but the average user doesn't need to fully understand the mathematical details to pick up the keyboard and start programming. Practically speaking, deep learning is not complicated, but the results are very objective. Teach you how to use deep learning: this is the purpose of this book.
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Deep Learning with R

Goodreads Rating: 4.59/5
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.
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Dive Into Deep Learning: Tools for Engagement

Goodreads Rating: 4/5
Learn about, improve, and expand your world of learning. This hands-on companion to the runaway best-seller, Deep Learning: Engage the World Change the World, provides an essential roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Loaded with tips, tools, protocols, and real-world examples, the easy-to-use guide has everything educators need to construct and drive meaningful deep learning experiences that give purpose, unleash student potential, and prepare students to become problem-solving change agents in a global society.
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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Goodreads Rating: 3.86/5
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.
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Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

Goodreads Rating: 4.31/5
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.
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Grokking Deep Learning

Goodreads Rating: 4.29/5
Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.
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Introduction to Deep Learning (The MIT Press)

Goodreads Rating: 3.83/5
This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.
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Neural Networks and Deep Learning: A Textbook

Goodreads Rating: 4.33/5
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.
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Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Goodreads Rating: -/5
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
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