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)

Getting Started with Deep Learning

In this chapter, we will discuss about some basic concepts of deep learning and their related architectures that will be found in all the subsequent chapters of this book. We'll start with a brief definition of machine learning, whose techniques allow the analysis of large amounts of data to automatically extract information and to make predictions about subsequent new data. Then we'll move onto deep learning, which is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.

Finally, we'll introduce deep learning architectures, the so-called Deep Neural Networks (DNNs)--these are distinguished from the more commonplace single hidden layer neural networks by their depth; that is, the number of node layers through which data passes in a multistep process of pattern recognition. we will provide a chart summarizing all the neural networks from where most of the deep learning algorithm evolved.

In the final part of the chapter, we'll briefly examine and compare some deep learning frameworks across various features, such as the native language of the framework, multi-GPU support, and aspects of usability.

This chapter covers the following topics:

  • Introducing machine learning
  • What is deep learning?
  • Neural networks
  • How does an artificial neural network learn?
  • Neural network architectures
  • DNNs architectures
  • Deep learning framework comparison