Book Image

Keras Reinforcement Learning Projects

By : Giuseppe Ciaburro
Book Image

Keras Reinforcement Learning Projects

By: Giuseppe Ciaburro

Overview of this book

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
Table of Contents (13 chapters)

Handwritten Digit Recognizer

The term handwriting recognition (HWR) refers to the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, and touchscreens. Written text can be detected on a piece of paper with optical scanning (Optical Character Recognition (OCR)) or intelligent word recognition. Alternatively, the movements of a pen tip can be detected, for example from a surface of the computer screen, a task that is generally easier as there are more clues available. These activities involve a wide range of practical applications, including the recognition of online spelling on tablet computers, recognition of the zip code to help sort sent mail, and the verification of signatures in the context of financial transactions.

In this chapter, you will work on building a handwritten digit recognizer in Python...