Python Reinforcement Learning Projects
Packt Publishing Limited (Verlag)
9781788991612 (ISBN)
Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries
Key Features
Implement Q-learning and Markov models with Python and OpenAI
Explore the power of TensorFlow to build self-learning models
Eight AI projects to gain confidence in building self-trained applications
Book DescriptionReinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.
In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.
By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
What you will learn
Train and evaluate neural networks built using TensorFlow for RL
Use RL algorithms in Python and TensorFlow to solve CartPole balancing
Create deep reinforcement learning algorithms to play Atari games
Deploy RL algorithms using OpenAI Universe
Develop an agent to chat with humans
Implement basic actor-critic algorithms for continuous control
Apply advanced deep RL algorithms to games such as Minecraft
Autogenerate an image classifier using RL
Who this book is forPython Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.
Sean Saito is the youngest ever Machine Learning Developer at SAP and the first bachelor hired for the position. He currently researches and develops machine learning algorithms that automate financial processes. He graduated from Yale-NUS College in 2017 with a Bachelors of Science (with Honours), where he explored unsupervised feature extraction for his thesis. Having a profound interest in hackathons, Sean represented Singapore during Data Science Game 2016, the largest student data science competition. Before attending university in Singapore, Sean grew up in Tokyo, Los Angeles, and Boston. Yang Wenzhuo is working as a Data Scientist at SAP, Singapore. He got a bachelor's degree in computer science from Zhejiang University in 2011 and a PhD degree in machine learning from National University of Singapore in 2016. His research focuses on optimization in machine learning and deep reinforcement learning. He has published papers on top machine learning/computer vision conferences including ICML and CVPR, and operations research journals including Mathematical Programming. Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of Technology – Madras. He has published articles in peer-reviewed journals and conferences and applied for few patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.
Table of Contents
Up and running with Reinforcement Learning
Balancing Cart Pole
Playing ATARI Games
Simulating Control Tasks
Building Virtual Worlds in Minecraft
Learning to Play Go
Creating a Chatbot
Generating a Deep Learning Image Classifier
Predicting Future Stock Prices
Looking Ahead
| Erscheinungsdatum | 06.10.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-13 | 9781788991612 / 9781788991612 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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