Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents

Buy

A practical guide to mastering reinforcement learning algorithms using Keras

Key Features

  • Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
  • Get to grips with Keras and practice on real-world unstructured datasets
  • Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning

Book Description

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.

What you will learn

  • Practice the Markov decision process in prediction and betting evaluations
  • Implement Monte Carlo methods to forecast environment behaviors
  • Explore TD learning algorithms to manage warehouse operations
  • Construct a Deep Q-Network using Python and Keras to control robot movements
  • Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset
  • Address a game theory problem using Q-Learning and OpenAI Gym

Who this book is for

Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book

Table of Contents

  1. Overview of Keras Reinforcement Learning
  2. Simulating random walks
  3. Optimal Portfolio Selection
  4. Forecasting stock market prices
  5. Delivery Vehicle Routing Application
  6. Prediction and Betting Evaluations of coin flips using Markov decision processes
  7. Build an optimized vending machine using Dynamic Programming
  8. Robot control system using Deep Reinforcement Learning
  9. Handwritten Digit Recognizer
  10. Playing the board game Go
  11. What is next?
(HTML tags aren't allowed.)

Tensors: The Mathematics of Relativity Theory and Continuum Mechanics
Tensors: The Mathematics of Relativity Theory and Continuum Mechanics
This book emerged from courses taught at the University College of Dublin, Carnegie-Mellon University and mostly at Simon Fraser University. This is a modern introduction to the theory of tensor algebra and tensor analysis. It discusses tensor algebra in Chapters 1 and 2. Differential manifold is introduced in Chapter 3. Tensor analysis,...
Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data
Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data

Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional...

Learning TypeScript
Learning TypeScript

Exploit the features of TypeScript to develop and maintain captivating web applications with ease

About This Book

  • Learn how to develop modular, scalable, maintainable, and adaptable web applications by taking advantage of TypeScript
  • Create object-oriented JavaScript that adheres to the...

Learning Docker - Second Edition: Build, ship, and scale faster
Learning Docker - Second Edition: Build, ship, and scale faster

Docker lets you create, deploy, and manage your applications anywhere at anytime – flexibility is key so you can deploy stable, secure, and scalable app containers across a wide variety of platforms and delve into microservices architecture

About This Book

  • This up-to-date edition...
Building Django 2.0 Web Applications: Create enterprise-grade, scalable Python web applications easily with Django 2.0
Building Django 2.0 Web Applications: Create enterprise-grade, scalable Python web applications easily with Django 2.0

Go from the initial idea to a production-deployed web app using Django 2.0.

Key Features

  • A beginners guide to learning python's most popular framework, Django
  • Build fully featured web projects in Django 2.0 through examples.
  • Deploy web applications in...
Mastering Exploratory Analysis with pandas: Build an end-to-end data analysis workflow with Python
Mastering Exploratory Analysis with pandas: Build an end-to-end data analysis workflow with Python

Explore Python frameworks like pandas, Jupyter notebooks, and Matplotlib to build data pipelines and data visualization

Key Features

  • Learn to set up data analysis pipelines with pandas and Jupyter notebooks
  • Effective techniques for data selection, manipulation, and...
©2020 LearnIT (support@pdfchm.net) - Privacy Policy