• Title: Deep Learning
• Author(s) Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Publisher: The MIT Press (November 18, 2016); eBook (Online Version - Free)
• Permission: This is the online version of the published book. It's Free!
• Hardcover: 775 pages
• eBook: HTML
• Language: English
• ISBN-10: 0262035618
• ISBN-13: 978-0262035613
• Share This:  

Book Description

This is the online version of the published book. It's Free!

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.

• Ian Goodfellow is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning.
• Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.
• Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.

• Amazon

• Deep Learning and Neural Networks
• Machine Learning
• Data Science
• Artificial Intelligence
• Data Analysis and Data Mining

• Deep Learning (Ian Goodfellow, et al)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Understanding Deep Learning (Simon J.D. Prince)

  An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Only the most important ideas to provide a high density of critical information in an intuitive and digestible form.

• The Principles of Deep Learning Theory: An Effective Approach

  This textbook establishes a theoretical framework for understanding deep learning models of practical relevance, provide clear and pedagogical explanations of how realistic deep neural networks actually work.

• The Shallow and the Deep: Introduction to Neural Networks

  This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

• Neural Networks and Deep Learning (Michael Nielsen)

  Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

• Neural Network Learning: Theoretical Foundations

  This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.

• Gradient Expectations: Structure of Predictive Neural Networks

  An insightful investigation into the mechanisms underlying the predictive functions of neural networks - and their ability to chart a new path for AI. Delve into the known neural architecture of the brain to illuminate the structure of predictive networks.

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• Dive into Deep Learning (Aston Zhang, et al.)

  This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

• Deep Learning for Coders with Fastai and PyTorch

  This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

• Deep Learning: A Visual Approach (Andrew Glassner)

  A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math.

• Machine Learning with Neural Networks (Bernhard Mehlig)

  This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.