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)

Accelerated Linear Algebra

The Accelerated Linear Algebra (XLA) is a domain specific compiler developed by TensorFlow for optimizing its computations. By this, you will get improvements in speed, memory usage and even portability on mobile platforms.

Initially, you won't see much benefit from XLA because it's still experimental but you can try it by using the just-in-time compilation or ahead-of-time compilations.

First, we are going to briefly mention the key strengths of TensorFlow and see how TensorFlow team had the challenge to keep and increase these key strengths.

Key strengths of TensorFlow

The following are the key strengths of TensorFlow:

  • Flexible: This Flexibility is coming from TensorFlow's nature of being interpreted. Also, you can see...