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)

Basic concepts of Reinforcement Learning

Reinforcement Learning (RL) aims to create systems that will learn and, at the same time, adapt to changes in the environment in which they are located, using a reward that is assigned to each action performed.

Software systems that process information in this way are called intelligent agents.

These agents decide to take an action based on the following:

  • State of the system
  • Learning algorithm used

To change the system state and maximize its long term rewards, and agent selects the action to be performed by continuously monitoring its environment.

To obtain a large reward and, therefore, optimize the Reinforcement Learning procedure, the agent must prefer actions that, in the past, have produced a good reward.

The actions are discovered, proving those never selected first. Therefore, the agent must exploit what it already knows, both to obtain the maximum reward, and also...