Deep Learning by MIT Press

Written by superstars in the field, this free and detailed introduction to Machine Learning and Deep Learning is intended for experienced practitioners as well as students. The book covers deep learning background, its techniques and algorithms, and research perspective.

Created by: Ian Goodfellow

Produced in 2016

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What you will learn

  • Learn to successfully apply deep learning techniques on Google Maps example
  • Commercial use of deep learning on speech recongition, computer vision, NLP
  • Understand deep learning through mathematical concepts
  • Basic machine learning algorithms such as linear regression
  • Important deep learning techniques like deep feedforward networks and convolutional networks
  • Choose the correct algorithm to improve your ML systems
  • Master linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods
  • Much, Much more!

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Quality Score

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Overall Score : 82 / 100

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Course Description

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machine learning Awards Best Text Based Course

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 SpaceXDeep 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.

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Pros

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Cons

    • Covers the latest developments of Deep Learning.
    • Clear and sophisticated presentation.
    • Considered as "the Bible" of Machine Learning.
    • Written by one of the most respected AI researchers.
    • Perfect as a reference for further learning and research.
    • The book is written in a high-level academic manner. Will be difficult to understand for some.
    • Not recommended for students that prefer step-by-step tutorials.

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Instructor Details

Ian Goodfellow

Ian Goodfellow is a Director of Machine Learning in the Special Projects Group, who previously worked at Google and OpenAI. He holds PhD in machine learning from the University of Montreal.

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Reviews

4.1

21 total reviews

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By Mkfs on 7/19/2017

This is apparently THE book to read on deep learning. Written by luminaries in the field - if you've read any papers on deep learning, you'll have encountered Goodfellow and Bengio before - and cutting through much of the BS surrounding the topic: like 'big data' before it, 'deep learning' is not something new and is not deserving of a special name. Networks with more hidden layers to detect higher-order features, networks of different types chained together in order to play to their strengths, graphs of networks to represent a probabilistic model.

By The_phet on 1/19/2018

If you are like me, someone who knows about ML or AI, and want to know about DL, or even if you are someone who doesn't know much about ML or AI, please run away from this now, don't do the mistake I did of devoting your time to a pointless process. I am not going to recommend any other book, because I don't want people to think I am here to shill, but there are way better options out there.If you want to learn DL: run away. In my opinion this book is for people who are already expets in DL and want to go deep into its theoretical background. If that is your case, then probably this is perfect for you. It is probably also very good for PhD students or researchers who are researching about DL. If you want to use DL as a final end-user: run away.

By Elie De Brauwer on 5/9/2016

The "i'm finished" should more or less be interpreted as "i've had it". This book is both awesome and horrible. It's awesome because it is giving an extremely up to date view on what is currently state of the art. At this moment the book isn't even't published and it will be a landmark once it hits the shelves. It is horrible because it diguises all insights in maths, and partical use/application should be sought after (instead of being plain obvious). The latter means that this book is really written as an academic text (and I'm not part of the target audience ;-) ).

By Ethan on 2/12/2018

This book is great for readers to gain intuition behind many of the concepts underpinning deep learning techniques taken for granted, with a focus on probabilistic graphical models towards the end.

By Tomasz Bartczak on 3/16/2017

A broad overview of the current state of deep learning. Given the introduction to machine learning in general it can be the position for learning "machine learning". Yet this is not a step-by-step tutorial, rather a place where one can start the reading and be redirected elsewhere for details.

By Luke Duncan on 11/27/2017

I both appreciated this book and was frustrated by it. If you want an academic survey paper with cited sources in the form of a book this is the book for you. If you want an approachable introduction this probably isnt it. The authors claim to be targeting two audiences: students and software engineers in industry. I think they understand the first audience much more than the second.

By Oleg Dats on 5/20/2019

If you ask me about only one book about Deep Learning I would suggest this one. It covers everything.

By Wojtekwalczak on 9/29/2018

Reading this book was tiresome. Imagine extracting the most technical pieces of hundreds of publications and piling them all together into a single book. This really is a prescription for unreadable manual, and that's unfortunately what has happened to "Deep Learning" book.

By Hampus Wessman on 7/17/2018

A very good high-level overview of the most popular deep learning techniques at the time. I will keep it around as a reference for sure.It requires some prior maths, statistics and machine learning knowledge, but is not a mathematical book with proofs and detailed abstract theory. The focus is on practically applicable theory at a high level, which it provides in a good way.

By Kirill on 11/15/2016

THE most rigorous and up to date reference of deep learning algorithms that is almost self-contained. Though If you intend to learn deep learning from scratch this book will not suffice

By Squidbot on 4/23/2016

The best advanced introduction textbook I ever read. Accessible and clear without being too watered down.

By Lee Richardson on 5/24/2019

A comprehensive overview of the Deep Learning paradigm, written by several leading researchers in the field. The author's cover many topics, and did a great job providing references to the current literature in the field. For this reason, I see this book more as a reference book than a book to read straight through. I read it straight through, but there were definitely some sections I skimmed over, especially when the author's introduced technical details of several related methods in the field.