For More Ebooks Visit NulledPremium >>> NulledPremium.com
Book details File Size: 21 MB Format: epub Print Length: 298 pages Page Numbers Source ISBN: 1788991613 Publisher: Packt Publishing; 1 edition (September 29, 2018) Publication Date: September 29, 2018 Sold by: Amazon.com Services LLC Language: English ASIN: B07F2S82W3
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 Description Reinforcement 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 for Python 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.
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 |
[NulledPremium.com] Python Reinforcement Learning
-
NulledPremium.com.url (0.2 KB)
-
Python Reinforcement Learning Projects by Sean Saito.epub (21.4 MB)
Websites you may like
-
1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url (0.3 KB)
-
2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url (0.3 KB)
-
3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url (0.2 KB)
-
4. (FTUApps.com) Download Cracked Developers Applications For Free.url (0.2 KB)
-
5. (Discuss.FTUForum.com) FTU Discussion Forum.url (0.3 KB)
-
How you can help Team-FTU.txt (0.2 KB)
files
|
udp://open.demonii.si:1337/announce udp://p4p.arenabg.com:1337/announce udp://tracker.torrent.eu.org:451/announce udp://tracker.cyberia.is:6969/announce udp://tracker.uw0.xyz:6969/announce udp://exodus.desync.com:6969/announce udp://explodie.org:6969/announce udp://denis.stalker.upeer.me:6969/announce udp://tracker.opentrackr.org:1337/announce udp://9.rarbg.to:2710/announce udp://tracker.tiny-vps.com:6969/announce udp://ipv4.tracker.harry.lu:80/announce udp://tracker.coppersurfer.tk:6969/announce udp://tracker.leechers-paradise.org:6969/announce udp://open.stealth.si:80/announce |