Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

Summary

TensorFlow is designed to make distributed machine and deep learning easy for everyone, but using it does require understanding some general principles and algorithms. Furthermore, the latest release of TensorFlow comes with lots of exciting features. Thus we also tried to cover them so that you can use them with ease. We have shown how to install TensorFlow on different platforms including Linux, Windows, and Mac OS. Before, covering this in even greater depth, we showed some example of how to upgrade the source code from the previous version of TensorFlow to the latest version 1.x.

In summary, here is a brief recap of the key concepts of TensorFlow explained in this chapter: