- Paperback: 384 pages
- Publisher: Manning Publications; 1st edition (22 December 2017)
- Language: English
- ISBN-10: 9781617294433
- ISBN-13: 978-1617294433
- ASIN: 1617294438
- Product Dimensions: 18.7 x 2 x 23.5 cm
- Customer Reviews: 390 customer ratings
- Amazon Bestsellers Rank: #3,001 in Books (See Top 100 in Books)
Deep Learning with Python Paperback – 22 December 2017
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The language used is very simple. Presentation of topics is well organised. It is very interesting to read with a computer by your side to practice the programming. Codes can be downloaded, of course.
Expects some familiarity with tensorflow and keras. A working knowledge of python is also necessary.
It is not a book for beginners.
My rating on this review is for the copy of the book. The Manning publication allows you to register and download the ebook. The codes are missing in this copy. It looks like a reprint. I own other Manning publication books and this book doesn't match the quality of those books.
Of course the content of the book needs no introduction, top notch.
If you want it go for it.
Top international reviews
If you want to get started with Keras, deep learning, neural networks and all that - this is one of the best books I've ever seen. If not the best.
It doesn't go full tilt into all the mathematics behind it - something I appreciate - but it sure gives you enough to get you started as well as a good way towards the more advanced subjects in this field. If you want all the formulas and algorithms behind this - there are better books but if you want to hit the ground running this is the book for you.
I don't think I can recommend this book highly enough.
The book contains real examples of Python/Keras code to do deep learning on standard data sets. Some knowledge of Python is required, but I think that any competent programmer can get this as they go along. I certainly improved my Python while working through the examples.
The author makes clear their belief that a Linux system is required to do the examples in the book. This is the author's only major mistake. I have tried the examples under Windows 10/Anaconda 3 and they simply work. Perhaps the GPU based examples work better under Linux - I didn't try these.
After finishing the book, the reader will be well placed to know the basics of deep learning, and to take the subject further.
Re the book. So far so good and it seems clearly and simply explained
The best AI book I have bought, with up to date explanations of what works / doesn't (mid 2017)
Very well written - really explains the key concepts well.
Together with the O'reilly hands on Scikit / Tensorflow book the best AI / deep learning primer to date
It starts with a series on simple practical examples which the reader can easily reproduce and explore alone. The explanations are readable and understandable away from a computer (I read much of it on holiday). It then goes into detail of the two most advanced applications of deep learning - image processing and text processing.
The notation throughout is python rather than formal mathematical notation. If you like reading code but don't like reading matrix equations, this will be ideal. The one possible shortcoming is that it veers heavily to the practical side and isn't concerned with the theory. Thus it doesn't explain how backprop works or even give you the equations, merely noting that most packages automate them so you might as well not waste your time and get on with learning how to do it. This is perhaps a wise approach since Hinton's excellent coursera lectures are freely available and are both accessible and rigorous.
The explanations around the examples are good and to the point. Code snippets were well written.
Concepts are formulated in a stimulating way that you will find yourself wanting more and more (at least I felt it this way). I couldn't shift my attention away from it.