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

3.88  ·  Rating details ·  75 ratings  ·  8 reviews
Kindle Edition, 1st, 298 pages
Published July 2015 by O'Reilly
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Abhishek
Oct 25, 2017 rated it liked it
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
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M. Cetin
Feb 18, 2019 rated it it was amazing  ·  review of another edition
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
...more
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.
Liamarcia Bifano
Feb 14, 2019 rated it really liked it
Strengths
- 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

Weaknesses
- Lacks math and precis
...more
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
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
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
Sep 28, 2015 rated it liked it
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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