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Top (max 10) reviews: Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python.

4.6 out of 5.0    6 total reviews.

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5.0 out of 5.0 -

by Amazon Customer on Jan. 2, 2018

I am very impressed with this book. In particular, for all my interns, the section on the mathematics (great introduction) and the genetle introductions are helpful. what is really great though is the code. Very well done, good examples to step through each concept, and all done in TensorFlow, with a good explanation of each example. Not much not to like about the book. Also love the coverage of AutoEncoders, which is a bit harder to find and which are becoming a hot topic for advanced solutions in Deep Learning. Thank you Santanu, great job!!!

3.0 out of 5.0 -

by Seth Harris on Jan. 23, 2018

I'm a beginner in the world of Machine Learning: not starting completely from scratch, but in the early stages of applying concepts to real-world data. This book covers a wide variety of topics concisely, but at the same time it's hard for me to imagine learning these principles from this book specifically. The book seems geared toward people who already have a strong grasp of the concepts it covers, despite its inclusion of mathematics brush-ups at the beginning.
My sense from looking through it is that it may prove useful as a reference book once the concepts have been learned from elsewhere. But as a first-time introduction, I think it might suit people with strong math backgrounds, as opposed to people with strong programming backgrounds. For the programmers who want to just get hacking and incorporate understanding as they go, my strong recommendation would be "Hands on Machine Learning with Scikit-Learn & TensorFlow" by Aurelien Geron.

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by prudvi raj on Dec. 20, 2017

I am working as a Data Scientist in the industrial space with 7+ years of experience. I have done a lot of courses on CourseEra and EDX but this is the best book I have come across in "Deep Learning". This is a book written by someone who spent years on deep learning and now trying to simplify deep learning for the world.
In the first chapter, the author introduces all the mathematical concepts that one needs to be familiar with which are pre-requisites to Neural networks. All the mathematical concepts are explained in an intuitive manner and at a granular level. It will help aspiring data scientists to learn these concepts very easily without the need to remember any formulas. In the second chapter, the author introduces the basics of Neural Networks and Tensor Flow. CNNs and RNNs are covered in depth in 3rd and 4th chapters. These chapters even include several popular architectures and Transfer Learning. Each and every concept is illustrated by practical examples followed by a how-to-do in python. Chapter 5 is about Restricted Boltzmann Machines and Auto-encoders. Chapter 6 is about Advanced Neural Networks dealing in Image Segmentation, classification, localization, and GANs.
Even if you are familiar with Deep Learning I still recommend this book as it deals with a lot of know concepts in detail and latest/advanced concepts in deep learning.

5.0 out of 5.0 -

by Jojo John on Dec. 20, 2017

I would highly recommend this book for all Deep Learning enthusiasts and budding data scientists. This is one of its kind book that has a profound focus on the math concepts required to explore the true depths of this field. The topics are well explained and neatly abstracts the complex math with the right depth. The book offers a rich collection of topics with a proportionate focus on supervised, unsupervised and semi-supervised learning. Examples used are extremely easy to understand and the codes are also extremely useful. Overall, I would say it was a joy reading this book.

5.0 out of 5.0 -

by Hayong Yun on Dec. 28, 2017

Promptly delivered. Good book.

5.0 out of 5.0 -

by sonia on Jan. 14, 2018

Great book for Deep Learning from a mathematical perspective.. Promptly delivered too !