- Paperback: 398 pages
- Publisher: Apress; 1st ed. edition (December 7, 2017)
- Language: English
- ISBN-10: 1484230957
- ISBN-13: 978-1484230954
- Product Dimensions: 7 x 0.9 x 10 inches
- Shipping Weight: 1.7 pounds (View shipping rates and policies)
- Average Customer Review: 7 customer reviews
- Amazon Best Sellers Rank: #34,212 in Books (See Top 100 in Books)
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Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python Paperback – December 7, 2017
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From the Back Cover
Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
- Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning
- Deploy complex deep learning solutions in production using TensorFlow
- Carry out research on deep learning and perform experiments using TensorFlow
About the Author
Santanu Pattanayak currently works at GE, Digital as a Senior Data Scientist. He has 10 years of overall work experience with six of years of experience in the data analytics/data science field and also has a background in development and database technologies. Prior to joining GE, Santanu worked in companies such as RBS, Capgemini, and IBM. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu is currently pursuing a master's degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also devotes his time to data science hackathons and Kaggle competitions where he ranks within the top 500 across the globe. Santanu was born and brought up in West Bengal, India and currently resides in Bangalore, India with his wife.
Top customer reviews
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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.
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.