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# Deep Learning

## (Adaptive Computation and Machine Learning)

by

**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

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ebook, Online draft, 787 pages

Published
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 a university ...more

* Following the success of back-propagation, neural network research gained popularity and reached a peak in the early 1990s. Afterwards, other machine learning

techniques became more popular until the modern deep learning renaissance that began in 2006.

* Regularization of an estimator works by trading increased bias for reduced variance.

* in neural networks, typically only the weight and not the biases are used in normalisation penalties

* Effect of ...more

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

This is a link to the website.
...more

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

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

My big takeaway from the book is that for my purposes I don’t need to understand these implementation details, even if I find them very interesting, because anything worth doing is implemented in libraries and by cloud providers. A hobbyist engineer like me can run co ...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

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