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

In this chapter, we introduced some of the fundamental themes of deep learning. It consists of a set of methods that allow a machine learning system to obtain a hierarchical representation of data, on multiple levels. This is achieved by combining simple units, each of which transforms the representation at its own level, starting from the input level, in a representation at a higher level, slightly more abstract.

In recent years, these techniques have provided results never seen before in many applications, such as image recognition and speech recognition. One of the main reasons for the spread of these techniques has been the development of GPU architectures, which considerably reduced the training time of DNNs. There are different DNN architectures, each of which has been developed for a specific problem. We'll talk more about those architectures in later chapters, showing examples of applications created with the TensorFlow framework.

The chapter ended with a brief overview of the implemented deep learning frameworks.

In the next chapter, we begin our journey into deep learning, introducing the TensorFlow software library. We will describe its main features and look at how to install it and set up a first working session.