
Best Deep Learning Books - ReDeiPirati
https://blog.floydhub.com/best-deep-learning-books-updated-for-2019/
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danieldk
The list does not describe why they are the best books, except for a very
short blurb. We read the Deep Learning book by Goodfellow, Bengio, and
Courville in our reading group when it came out. Even though it contains
useful information, it is written in a very haphazard fashion. It is also very
unclear what its target audience is. Some sections start as a foundational
description, to suddenly change into something that is only for readers with a
strong maths background. No one in the reading group was enthusiastic about
the book and most actively recommend against it (some called it 'the deep
learning book for people who already know deep learning').

The highest-rated Amazon reviews seem to have come to the same conclusion:
[https://www.amazon.com/Deep-Learning-Adaptive-Computation-
Ma...](https://www.amazon.com/Deep-Learning-Adaptive-Computation-
Machine/dp/0262035618/ref=sr_1_1)

Put differently, a list such as the linked one may attract a lot of visitors.
But without critical, in-depth reviews it is not very useful and might set
potential learners on the wrong path.

~~~
freyir
> it is written in a very haphazard fashion

I felt the same way. Knowledgeable authors, loads of information, but quite
poorly written.

That said, I don’t know of another book that’s as up to date or comprehensive,
so I guess we’re stuck with it till something better comes along.

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brylie
Deep Learning with Python by Francois Chollet is very accessible as an
introductory text. It uses plain language, avoids heavy math, and provides
hands-on experience for the reader.

In general, I have found Manning to be one of the best technical publishers in
terms of quality of content and updates.

~~~
dominotw
I credit Manning for my tech career. I read read Jon skeet c# and Fogus
clojure books put me on another level.

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mkagenius
For people who are a little afraid of the word deep learning:

It is not very difficult, in fact it is a borderline easy concept. So, do not
be afraid in case you are. The mathematics behind deep learning is a little
complicated but not much.

But if you just want to have a working knowledge then its quite approachable
even for beginners and you do not need to learn the mathematics at all. In
fact even the professionals rarely worry about the mathematics (they should
though).

~~~
a_bonobo
In the fast.ai course, there are two or three amazing videos where Jeremy
Howard explains the basic concepts of deep learning (convolutions in this
case) using Excel spreadsheets, highly recommended.

I agree with mkagenius, I've played around a lot with deep learning and like
physics it suffers a lot from unnecessary jargon that hides relatively simple
concepts, for example this random quote:

>ReLU stands for rectified linear unit, and is a type of activation function.
Mathematically, it is defined as y = max(0, x).

Now everybody could just use plain English - if you have a negative number,
set it to zero, in all other cases, keep the number - I don't know why someone
has to use 'rectified linear unit' to describe this simple operation.

~~~
anon946
Intuitive descriptions using words have a place, and I do wish there were more
of that. However, if you favor words over math notation, at some point you
still have to call it something. What would you call it?

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jphoward
For anyone who knows intermediate python and numpy and wants to learn how
neural networks (CNNs, RNNs) work through simple pythonic examples, Chollet’s
book is excellent. I’ve bought two copies as I can’t bear lending mine out to
my students. He’s the creator of the Keras library and it’s beautiful in its
symplicity.

