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)

Q-learning algorithm

Solving a Reinforcement Learning problem during the learning process estimates an evaluation function. This function must be able to assess, through the sum of the rewards, the convenience or, otherwise, a policy. The basic idea of Q-learning is that the algorithm learns the optimal evaluation function on the whole space of states and actions (SxA).

The so-called Q-function provides a match in the form Q: S × A => V, where V is the value of future rewards of an action, a Î A, executed in the state s Î S.

Once it has learned the optimal function, Q, the agent will of course be able to recognize what action will lead to the highest future reward in a s state.

One of the most used examples for implementing the Q-learning algorithm involves the use of a table. Each cell of the table is a value, Q(s; a)= V, initialized to 0.

The agent can perform any action a Î A, where A is...