Deep Learning

This book PDF is perfect for those who love Computers genre, written by Ian Goodfellow and published by MIT Press which was released on 10 November 2016 with total hardcover pages 801. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Deep Learning books below.

Deep Learning
Deep Learning
Author : Ian Goodfellow
File Size : 47,9 Mb
Publisher : MIT Press
Language : English
Release Date : 10 November 2016
ISBN : 9780262337373
Pages : 801 pages
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Deep Learning by Ian Goodfellow Book PDF Summary

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. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville

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

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Learning Deep Learning

Learning Deep Learning by Magnus Ekman

NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr.

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Deep Learning for Coders with fastai and PyTorch

Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library

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Machine Learning with PyTorch and Scikit Learn

Machine Learning with PyTorch and Scikit Learn by Sebastian Raschka,Yuxi (Hayden) Liu,Vahid Mirjalili,Dmytro Dzhulgakov

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key FeaturesLearn applied machine learning with a solid foundation

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The Principles of Deep Learning Theory

The Principles of Deep Learning Theory by Daniel A. Roberts,Sho Yaida,Boris Hanin

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

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Introduction to Deep Learning

Introduction to Deep Learning by Eugene Charniak

A project-based guide to the basics of deep learning. 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

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Deep Learning

Deep Learning by John D. Kelleher

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from

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Programming Machine Learning

Programming Machine Learning by Paolo Perrotta

You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master

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