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Neural Networks and Deep Learning

4.49  ·  Rating details ·  209 ratings  ·  36 reviews
Neural Networks and Deep Learning is a free online book. The book will teach you about:
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many p
Published November 25th 2013

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4.49  · 
Rating details
 ·  209 ratings  ·  36 reviews

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A.N. Mignan
Jun 25, 2018 rated it it was amazing
Shelves: science-tech
Thanks to this book, I can finally build my own neural net from scratch, not just run one line of code on tensorflow, caret or keras to get what I need. This is really a great tutorial: all codes provided, step-by-step improvement of an ANN to predict the MNIST dataset, interactive plots to understand the basics of neural nets, etc. It is easy to read, gives some good insights into the historical problems encountered in this field of research and how those problems got resolved. The concepts are ...more
Jan 19, 2015 rated it really liked it
Shelves: computer-science
The book is still a work in progress, so don't take this review too seriously.

I really liked how diligently Nielsen explains the hows and whys of neural networks. If, like I used to, you fear the magic of backpropagation, this book will make every effort to dissolve your fears. An immensely careful and thorough treatment of backprop is probably the book's most valuable chapter. However, later on Nielsen starts making a lot of detours. Of course, the field of neural networks is barely developed a
Tobi Lehman
Apr 14, 2017 rated it it was amazing
I enjoyed the level of care Michael Nielson put into explaining the mathematics behind this exciting subject. The web version of the book is very well built too, with on-demand formulae displaying in the margin when you click on them, so you don't have to scroll back to re-read them.

A person without a background in linear algebra and calculus would not get much from the book, but that's not a problem with the book, those topics are a good thing for everyone to study anyway. This book also provid
Priyansh Trivedi
Aug 04, 2016 rated it it was amazing
The first book I've read on the topic. The author carefully lays out a narrative which helps one grasp the entire picture, piece by piece. He works with a (very popular) running example as the concepts are broken down to you. Steers clear of daunting mathematical proofs but doesn't shy away from logical discourses some of which span an entire chapter.

This is by no way a comprehensive book on everything that is happening on the field, but if you, like me are someone who's just begun and is looki
Farsan Rashid
May 05, 2018 rated it it was amazing
Shelves: machine-learning
TLDR: Extraordinary for intended readers. The book is intended for readers who wants to understand how/why neural networks work instead of using neural network as a black box.

The book consists of six chapters, first four covers neural networks and rest two lays the foundation of deep neural network. Lots of animations, pictures, interactive elements all over the book makes you visualize neural network right in front of your eyes. Time to time reference of research papers are given for enthusias
Ankit Solanki
Jan 04, 2018 rated it really liked it
Shelves: read-in-college
Michael explains the simplistic questions, whys, and hows of Neural Network in a fluent practical way. His clarity about this field and the future of artificial intelligence is appreciable. Michael's book talks to the reader quite often and goes through the thinking procedure till getting the Idea. The thing that I love the most about this book is, it not only explains the facts, it takes to a tour of why we do what we do? how did it get discovered in the first place?. The Appendix discussion ab ...more
Maru Kun
Sep 18, 2018 marked it as bubbling-under
The website link is here
Alex Aldo
Feb 08, 2019 rated it it was amazing
Absolutely amazing! A must read for everyone who wants to kick-start her/himself how neural Networks work, with some basic understanding of how to implement them in python. Many useful exercises and problems test your understanding and motivate to continue.
Aug 15, 2016 rated it it was amazing
The books most redeeming quality is that the author anticipates the follow up questions the reader might have and keeps on answering them. As a result, one gets a much more complete idea about the individual chapters/book.

On top of that, the exposition is relatively accessible and easy to follow, although there is a few weak spots. The book does get technical (calculus), but it also explains most formulas in plain English and makes it easy to fast forward and just get a rough idea of what's goin
Oct 29, 2018 rated it it was amazing
Shelves: textbooks
OK, so I write book reviews for my textbooks. Hope that's cool here. I'm getting prepared for the upcoming winter quarter where I will be teaching my first course ("Data Science Methods for Clean Energy Research") pretty much solo. I'm super stoked, but also feeling a tad of imposter syndrome: I only took a short intro course into data science, and the rest is self-taught using Python in my own research. I have never taken a formal course on the material I will be teaching. OK, so some of it, ye ...more
Apr 11, 2018 rated it really liked it
This stands out among the common introductions to machine learning with artificial neural networks because the author's presentation of the mathematical formalism is refreshingly clear and precise. Symbols do not suddenly change their meanings and indices are well defined. The author does not gloss over the mathematics, but enhances derivations with heuristic explanations and interactive illustrations that both serve well to strengthen intuitions. I guess Nielsen's background in theoretical phys ...more
Mar 11, 2019 rated it it was amazing
Simple and informative introduction to many of the most important concepts in this rapidly developing field.

While I read this for the technical details, I was also struck by some of the more abstract discussions on intelligence. For example, this quote stuck with me

"In the early days of AI research people hoped that the effort to build an AI would also help us understand the principles behind intelligence and, maybe, the functioning of the human brain. But perhaps the outcome will be that we en
Feb 03, 2019 rated it really liked it
A nice introduction to foundational aspects of NN and DL. The use of interactive graphics helps convey the intuition behind the workings of NN and DL. Even so, being familiar with math (calculus and linear algebra) and being patient will help get the most out of the book. That said, I applaud the author's attempt to make to simplify the topic. A good online resource to start learning about NN and DL.
Mario Aburto
May 13, 2017 rated it it was amazing
A very good book to understand the basics of neural networks, the problems that come with them and some improvements to address those problems. The explanations are very clear, and the text is easy to follow.

Additionally, once read, the text is useful for future reference.
Jin Shusong
Jul 26, 2017 rated it it was amazing
Very helpful. A step-by-step neural network illustration book.
Hongliang Ma
Oct 11, 2017 rated it it was amazing
Very good introductory book to deep learning.
Apr 09, 2018 rated it it was amazing
Great introduction to this topic.
Ajay Palekar
Feb 18, 2018 rated it liked it
A tough read, filled with an intuitive grasp of Neural networks and detailed explanation of the algorithims and programming that make them possible
Yang Zhang
Jul 15, 2018 rated it it was amazing
Shelves: deep-learning
Good book to start with neural networks.
Hashem Koohy
Jan 02, 2017 rated it really liked it
This is very good book, providing a solid mathematical background and techniques used in most neural network (deep learning) algorithms as well as demonstrating how to implement these techniques.
Ravindra Pai
Jan 11, 2017 rated it it was amazing
Shelves: awesome-books
A must read for every Neural net and deep learning enthusiast. Michael Neilsen has unique ability taking a difficult subject and narrate it in easy to understand way. Book doesn't dilute mathematics and neural net is implemented from scratch. Please make sure that you read all the chapters and don't skip math or code. It's really good book to read if one has finished Andrew Ng's Machine learning class and before taking CS231n Stanford Conv-net class,(currently I am taking it).

Many people might i
Dec 11, 2016 rated it really liked it
Shelves: mathematics
Good introduction to neural networks mostly. The deep learning is only shortly discussed in the last chapter. I would rather say that it is a very long introductory article to a topic that is getting more and more importance in data science. Though it is a branch of mathematics you don't need to know any maths beyond calculus. It has a conversational type of writing, and explains formulaes in detail. Basically you can read it before going to sleep. Nice thing about the web version is that all gr ...more
Dec 26, 2016 rated it really liked it
Very interesting book and read by Michael Nielsen. It offers a rather comprehensive overview on basic machine learning algorithms, teaching you not only to build your own but to improve it as you may need. It also features a final chapter and appendix with several applications of neural networks and how they have managed to surpass other competing ML algorithms, featuring (as would be expected) a small section on artificial inteligence.

By using Python I'm guessing this is a book most programmers
Apr 11, 2016 rated it it was ok
Shelves: programming
Neural nets are a really cool concept to learn about, but I would not have gotten through this at all without book club. It's pretty mathy, because the author is trying to get readers to develop an intuition, but I would've rather there have been more of the plain language description of what you're doing with the formulas annotated for what each part means. I'd recommend the "Neural Networks Demystified" YouTube series by Welch Labs instead.
Dec 16, 2016 rated it liked it
Shelves: non-fiction
Good introduction to neural networks that is more beginner-friendly than most. You'll still need a good understanding of calculus if you want to understand the more technical bits (chapter 2 in particular). Another good (non-book) source for this material is the excellent online Stanford course CS231n.
Lara Thompson
Jul 21, 2016 rated it it was amazing
Shelves: technical
Very well written introduction to neural networks, including regularization, choosing hyperparameters, convolution/pooling, code included both from scratch using numpy and a more optimized version using theano. Links to many influential papers, ongoing research and other related tutorials. Highly recommend!
Charlie Brummitt
May 18, 2016 rated it it was amazing
This is the way I like to learn new concepts: with lots of intuition and pictures. This is a great way to get an intuitive grasp of artificial neural networks and deep learning. It's well written and conversational in tone.
Tushar Gupta
Jan 18, 2017 rated it it was amazing
Book is very good for beginners. Basic neural networks has been explained in depth and with very good and innovative explanations.
May 06, 2016 rated it really liked it
Clear, concise, and intuitive explanations.

Incredible resource, freely available on Nielsen's website, we surely live in exciting times!
Jul 13, 2016 rated it really liked it
gentle introduction to modern neural networks
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