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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 fro ...more

ebook, Online draft, 787 pages

Published
2016
by The MIT Press

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Showing 1-30

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 a university ...more

Sep 18, 2018
Maru Kun
marked it as to-read

This is a link to the website.

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-explanatory. Bu ...more

Jul 17, 2018
Hampus Wessman
rated it
really liked it
·
review of another edition

Shelves:
non-fiction,
maths-cs

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

What really astounds me about this book is not its quality

*per se*- it's that 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 into the theo ...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 practical i ...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.

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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