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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

3.90  ·  Rating details ·  62 ratings  ·  8 reviews

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

Companies such as Google, Microsoft, and Facebook

Kindle Edition, 298 pages
Published May 25th 2017 by O'Reilly Media (first published July 1st 2015)
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3.90  · 
Rating details
 ·  62 ratings  ·  8 reviews

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M. Cetin
Feb 18, 2019 rated it it was amazing
Its one of the few books, that combines practical and theoretical information in a very balanced way. The first half of the book for me was very easy to follow. But I need to add, before the book, I have finished Andrew Ng's 16-week Machine Learning course, read a couple other books on Data Science and did some basic math&coding on the various ML/AI areas.

Somehow, up to Convolutional Neural Networks (~%50 of the book), there is a very good overview of what Gradient Descent is and how to imp
Sweemeng Ng
Aug 11, 2018 rated it really liked it  ·  review of another edition
If you expect code example, you would be disappointed. This book is very good at covering fundamentals, which I like. I suggest this book as a supplement with other deep learning book.
Oct 25, 2017 rated it liked it  ·  review of another edition
Shelves: non-fiction
When in school, we often used a term to label things that were hard to comprehend - OHT or “Over Head Transmission”. Essentially, concepts that the brain failed to catch. This book felt the same at many levels.

It was great once again encounter calculus, vectors, transforms and matrices, long after school and college days. I can’t say I understood them with the same rigor as when in school though. Reading this book didn’t help me understand Neural Networks all that much as it made me familiar wi
Liamarcia Bifano
Feb 14, 2019 rated it really liked it  ·  review of another edition
- Gives a really good overview of computer vision history and why traditional machine learning methods don't perform as good as convolutional networks
- The section that talks about Gradient Descent is really well explained and destroy some myths around gradient descent (even though there is no math)
- Gives a clear and intuitive idea of how convolutional layers can capture patterns in images
- It includes attention methods for NLP

- Lacks math and precise definitions (but that
Phil Tomson
Sep 19, 2018 rated it really liked it  ·  review of another edition
This book strikes a good balance between the DL textbooks which are quite dense and the many practitioners guides which have code examples but are light on theory & math. There are equations here as well as code. I've been checking this one out from the library, but I'm going to go ahead an order my own copy.
Vladimir Rybalko
Nov 07, 2018 rated it it was ok
Shelves: data-science
As for me, it's a slightly complicated. The math basic is explained in a quite poor and boring manner. The another disadvantage is a lack of real world examples. It's a challenge to connect a pure formulas with high level ML algorithms. I agree the book might be useful however I don't like so academic style. As result this is only two stars. I can't give more.
Bing Wang
Sep 21, 2017 rated it really liked it  ·  review of another edition
not read chapter 8. good start point to read open AI gym. This book does not provide much details about each algorithm. It basically just mentions what it is. Therefore, read multiple books at the same time is a great help to understand how deep learning works. Some codes syntax are old and should be corrected. However, it definitely worths time reading the example codes.
Cario Lam
I am finished with the number of chapters that have been released so far. There have been three in total. The material is a little rough but it is an early release. One should have some basic understanding of statistics and probability before attempting to digest the material. Looking forward to the additional chapters.
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