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# Deep Learning

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**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.**

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 ex ...more

Hardcover, 778 pages

Published
December 9th 2016
by The MIT Press

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Part 2: the part I like the most. It includes almost everything we need to know to adapt deep learning algorithms to practical matters.

Part 3: still feeling meh. It's too difficult for me to understand at this moment. Maybe I will come back after finishing PRML book.

Before I go on detail, just a bit about my background. I am a Comp Sci researcher in my early 30s, working in ...more

This is a link to the website.

Sep 29, 2018
Wojtekwalczak
rated it
it was ok
·
review of another edition

Shelves:
neural-networks,
machine-learning

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. I definitely prefer reading articles (including brilliant articles by the Authors of this book).

The negative side is that it obfuscates its information by its presentation. It's not motivated well -- if I wasn't already familiar with most of it, it might have been harder to grasp, but I can't test that hypothesis. Some people complain about the math in the reviews -- I don't as math can be self-exp ...more

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. Look elsewhere both for practical instructions on how to use various tools and frameworks (e.g. Tensorflow) an ...more

Mar 08, 2018
Frank Palardy
rated it
really liked it
·
review of another edition

Shelves:
python-algorithms

This tries to be the clr of deep learning. But it might be too early for that so the last part is more experimental. Also, statistics is different than real math so all the proofs don't make much sense.

May 24, 2019
Lee Richardson
rated it
it was amazing
·
review of another edition

Shelves:
math-statistics,
ai

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.
...more

What really astounds me about this book is not its quality

*per se*- it' ...more

It is not an easy book. If you do not have a sound mathematical background it will be very hard. The author does a great job in one of the first chapter in providing such background in a sound way.

This is not to be considered a simple tutorial to build your machine learning algorithm. This book can be a resource both for practitioner and for researchers since it goes deep i ...more

I can personally recommend this book for anyone who wants to use deep learning in his company or fo ...more

The first chapters of the book are a great intro into the fundamentals, but as I progressed through the book, it felt like a list of topics, with a hint of math (I had hoped to get the raw details) and very few ...more

To partly quote the book itself on its target audiences: students interested in AI/machine learning and software engineers who begin to use deep learning for their products.

Next on the list: Try out TensorFlow.

Jul 12, 2018
Edward Barker
rated it
it was amazing
·
review of another edition

Shelves:
summer-learning

Really well structured. Includes a knowledge tree to explain where you can use the book for reference, and where certain chapter are dependent on others. Only criticism is the lack of depth of explanation for linear Algebra and probability theory, but for a book that is not about these subjects, definitely excusable.

Make sure you know vector calculus, probability theory, and linear Algebra before reading this...

Fantastic and informative

Make sure you know vector calculus, probability theory, and linear Algebra before reading this...

Fantastic and informative

The author of this book is leading deep learning scientist recently.

If you are a curious person without any computing knowledge, It is not recommended.

But if you are IT person or IT researcher with rich experience, this book is highly recommended to you.

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