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)

Reinforcement Learning

Reinforcement Learning is based on an interesting psychological theory:

Applying a reward immediately after the occurrence of a response increases its probability of reoccurring, while providing punishment after the response will decrease the probability (Thorndike, 1911).

A reward, received immediately after the execution of a correct behavior, increases the likelihood that this behavior will be repeated; while, following an undesired behavior, the application of a punishment decreases the likelihood of that error reocurring. Therefore, once a goal has been established, Reinforcement Learning seeks to maximize the rewards received, to achieve the designated goal.

RL finds applications in different contexts in which supervised learning is inefficient.

A very short list includes the following:

  • Advertising helps in learning rank, using one-shot learning for emerging items, and new users will...