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)

General overview

TensorFlow is an open source software library for numerical computation using data flow graphs that enables machine learning practitioners to do more data-intensive computing. It provides some robust implementations of widely used deep learning algorithms. Nodes in the flow graph represent mathematical operations. On the other hand, the edges represent multidimensional tensors that ensure communication between edges and nodes. TensorFlow offers you a very flexible architecture that enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

What's new with TensorFlow 1.x?

The APIs in TensorFlow 1.0 have changed in ways that are not all backward-compatible. That is, TensorFlow programs...