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Ebook Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow + Kèm Code

15 tháng 9 2019 / No Comments


Leverage the power of reward-based training for your deep learning models with Python
Key Features.

  • + Use Q-learning to train deep learning models using Markov decision processes (MDPs).
  • + Study practical deep reinforcement learning using deep Q-networks.
  • + Explore state-based unsupervised learning for machine learning models.
Book Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. 

This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as frameworks such as Keras and TensorFlow. 
A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym's CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you'll gain a sense of what's in store for reinforcement learning. 
By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.

What you will learn:
  • + Explore the fundamentals of reinforcement learning and the state-action-reward process
  • + Understand Markov decision processes
  • + Get well versed with frameworks such as Keras and TensorFlow
  • + Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and + OpenAI Gym
  • + Choose and optimize a Q-Network's learning parameters and fine-tune its performance
  • + Discover real-world applications and use cases of Q-learning
Who this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.

Table of Contents
  1. Brushing Up on Reinforcement Learning Concepts.
  2. Getting Started with the Q-Learning Algorithm.
  3. Setting Up Your First Environment with OpenAI Gym.
  4. Teaching a Smartcab to Drive Using Q-Learning.
  5. Building Q-Networks with TensorFlow.
  6. Digging Deeper into Deep Q-Networks with Keras and TensorFlow.
  7. Decoupling Exploration and Exploitation in Multi-Armed Bandits.
  8. Further Q-Learning Research and Future Projects.
  9. Assessments.
LINK DOWNLOAD EBOOK PDF 1
LINK DOWNLOAD CODE 1

LINK DOWNLOAD EBOOK PDF 2

LINK DOWNLOAD CODE 2


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Chia Sẻ Ebook Excel 2019 Bible [PDF]

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The complete guide to Excel 2019
Whether you are just starting out or an Excel novice, the Excel 2019 Bible is your comprehensive, go-to guide for all your Excel 2019 needs. Whether you use Excel at work or at home, you will be guided through the powerful new features and capabilities to take full advantage of what the updated version offers. Learn to incorporate templates, implement formulas, create pivot tables, analyze data, and much more.
Navigate this powerful tool for business, home management, technical work, and much more with the only resource you need, Excel 2019 Bible.
  • + Create functional spreadsheets that work.
  • + Master formulas, formatting, pivot tables, and more.
  • + Get acquainted with Excel 2019's new features and tools.
Whether you need a walkthrough tutorial or an easy-to-navigate desk reference, the Excel 2019 Bible has you covered with complete coverage and clear expert guidance.

LINK DOWNLOAD EBOOK PDF 1
LINK DOWNLOAD EBOOK PDF 2


Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.

Tuyên bố miễn trừ bản quyền:
Trang web này không lưu trữ bất kỳ tệp nào trên máy chủ của nó. Chúng tôi chỉ lập chỉ mục và liên kết đến nội dung được cung cấp bởi các trang web khác. Vui lòng liên hệ với các nhà cung cấp nội dung để xóa nội dung bản quyền nếu có và gửi email cho chúng tôi, chúng tôi sẽ xóa các liên kết hoặc nội dung có liên quan ngay lập tức.