Introduction To Reinforcement Learning With Python
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Hands On Reinforcement Learning With Python
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Author : Sudharsan Ravichandiran
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-28
Hands On Reinforcement Learning With Python written by Sudharsan Ravichandiran and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-28 with Computers categories.
A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. What you will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman’s optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN Who this book is for If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
Introduction To Reinforcement Learning With Python
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Author : Greyson Chesterfield
language : en
Publisher: Independently Published
Release Date : 2024-12-11
Introduction To Reinforcement Learning With Python written by Greyson Chesterfield and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-11 with Computers categories.
"The AI Revolution: Mastering Reinforcement Learning with Python" is a groundbreaking guide that transforms complex AI concepts into practical, real-world applications. Written by renowned AI expert Greyson Chesterfield, this comprehensive masterpiece demystifies the cutting-edge field of reinforcement learning through clear explanations and hands-on Python implementations.From autonomous vehicles to stock market predictions, this book guides you through building intelligent systems that learn and adapt. Whether you're a software engineer looking to upgrade your skills or a data scientist seeking to master AI, you'll discover how to create sophisticated algorithms that power today's most innovative technologies.What You'll Master: Building intelligent trading systems for financial markets Creating game-winning AI agents for complex environments Developing self-learning robotics applications Implementing state-of-the-art deep reinforcement learning algorithms Exclusive Features: Ready-to-use Python code examples Real-world case studies from tech giants Step-by-step tutorials for building AI systems Advanced techniques used in modern AI applications
Ai Crash Course
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Author : Hadelin de Ponteves
language : en
Publisher:
Release Date : 2019-11-28
Ai Crash Course written by Hadelin de Ponteves and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Computers categories.
This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. Key Features Roll up your sleeves and start programming AI models No math, data science, or machine learning background required Packed with hands-on examples, illustrations, and clear step-by-step instructions 5 hands-on working projects put ideas into action and show step-by-step how to build intelligent software Book Description AI is changing the world - and with this book, anyone can start building intelligent software! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Taking a graduated approach that starts with the basics before easing readers into more complicated formulas and notation, Hadelin helps you understand what you really need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming: Google Colab Python TensorFlow Keras PyTorch AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learn Master the key skills of deep learning, reinforcement learning, and deep reinforcement learning Understand Q-learning and deep Q-learning Learn from friendly, plain English explanations and practical activities Build fun projects, including a virtual-self-driving car Use AI to solve real-world business problems and win classic video games Build an intelligent, virtual robot warehouse worker Who this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).
Foundations Of Deep Reinforcement Learning
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Author : Laura Graesser
language : en
Publisher: Addison-Wesley Professional
Release Date : 2019-11-20
Foundations Of Deep Reinforcement Learning written by Laura Graesser and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Computers categories.
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Python Reinforcement Learning
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Author : Sudharsan Ravichandiran
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-18
Python Reinforcement Learning written by Sudharsan Ravichandiran and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-18 with Computers categories.
Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key FeaturesYour entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various RL and DRL algorithmsExplore the power of modern Python libraries to gain confidence in building self-trained applicationsBook Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan RavichandiranPython Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa ShanmugamaniWhat you will learnTrain an agent to walk using OpenAI Gym and TensorFlowSolve multi-armed-bandit problems using various algorithmsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach your agent to play Connect4 using AlphaGo ZeroDefeat Atari arcade games using the value iteration methodDiscover how to deal with discrete and continuous action spaces in various environmentsWho this book is for If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.
Reinforcement Learning With Python
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Author : Anthony Williams
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-09-24
Reinforcement Learning With Python written by Anthony Williams and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-24 with categories.
Reinforcement Learning with Python Reinforcement learning is one of those data science fields, which will most certainly shape the world. The changes are already visible since we have self-driving cars, robots and much more we used to see only in some futuristic movies. Reinforcement learning is widely used machine learning technique, a computational approach when it comes to the different software agents, which are trying to maximize the total amount of possible reward they receive while interacting with some uncertain as well as very complex environments. This book is divided into seven chapters in which you will get to reinforcement techniques and methodology better. The first chapters will introduce you to the main concept laying being reinforcement learning techniques. Further, you will see what is the difference between reinforcement learning and other machine learning techniques. The book also provides some of the basic solution methods when it comes to the Markov decision processes, dynamic programming, Monte Carlo methods and temporal difference learning. The book will definitely be your best companion as soon as you start working on your own reinforcement learning project in Python and you will realize that these learning techniques are our future. Even now, it is impossible to imagine our world without advancements in machine learning concept. The concept or reinforcement learning even though being present for many decades, has reached its peak only a couple of years ago. Many industries have been presenting amazingly innovative machines, robots and much that we saw only in the futuristic movies. I understand why this topic excites you, and if you have decent programming skills and of course a desire to embark on this adventure, the book will provide you an amazing start on your journey. What you will learn by reading this book: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search Dynamic programming in Python for policy evaluation, policy iteration and value iteration Temporal difference learning or TD And much, much more... Get this book NOW and learn more about Reinforcement Learning with Python!
Ai Crash Course
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Author : Hadelin de Ponteves
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-29
Ai Crash Course written by Hadelin de Ponteves and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.
Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key FeaturesLearn from friendly, plain English explanations and practical activitiesPut ideas into action with 5 hands-on projects that show step-by-step how to build intelligent softwareUse AI to win classic video games and construct a virtual self-driving carBook Description Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learnMaster the basics of AI without any previous experienceBuild fun projects, including a virtual-self-driving car and a robot warehouse workerUse AI to solve real-world business problemsLearn how to code in PythonDiscover the 5 principles of reinforcement learningCreate your own AI toolkitWho this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).
Reinforcement Learning For Finance
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Author : Yves J Hilpisch
language : en
Publisher:
Release Date : 2024-12-03
Reinforcement Learning For Finance written by Yves J Hilpisch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Business & Economics categories.
Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of reinforcement learning methods in finance. Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems. This book covers: Reinforcement learning Deep Q-learning Python implementations of these algorithms How to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocation This book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance. Dr. Yves Hilpisch is founder and CEO of The Python Quants, a group that focuses on the use of open source technologies for financial data science, AI, asset management, algorithmic trading, and computational finance.
Deep Learning With Python
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Author : Chao Pan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-06-14
Deep Learning With Python written by Chao Pan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-14 with categories.
***** BUY NOW (will soon return to 24.77 $) *****Are you thinking of learning deep Learning using Python? (For Beginners Only) If you are looking for a beginners guide to learn deep learning, in just a few hours, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach, which would lead to better mental representations.Step-by-Step Guide and Visual Illustrations and ExamplesThis book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Book Objectives This book will help you: Have an appreciation for deep learning and an understanding of their fundamental principles. Have an elementary grasp of deep learning concepts and algorithms. Have achieved a technical background in deep learning and neural networks using Python. Target UsersThe book designed for a variety of target audiences. Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and deep learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Understanding Machine Learning Models Evaluation of Machine Learning Models: Overfitting, Underfitting, Bias Variance Tradeoff Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras A First Look at Neural Networks in Keras Introduction to Pytorch The Pytorch Deep Learning Framework Your First Neural Network in Pytorch Deep Learning for Computer Vision Build a Convolutional Neural Network Deep Learning for Natural Language Processing Working with Sequential Data Build a Recurrent Neural Network Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Deep Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is an excellent book, it is a very good introduction to deep learning and neural networks. The concepts and terminology are clearly explained. The book also points out several good locations on the internet where users can obtain more information. I was extremely happy with this book and I recommend it for all beginners" - Prof. Alain Simon, EDHEC Business School. Statistician and DataScientist.
Introduction To Machine Learning With Python
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Author : David James
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-08-25
Introduction To Machine Learning With Python written by David James and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-25 with categories.
***** BUY NOW (will soon return to 24.78 $)******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and underfitting correctness The Bias-Variance Trade-off Feature Extraction and Selection A Regression Example: Predicting Boston Housing Prices Import Libraries: How to forecast and Predict Popular Classification Algorithms Introduction to K Nearest Neighbors Introduction to Support Vector Machine Example of Clustering Running K-means with Scikit-Learn Introduction to Deep Learning using TensorFlow Deep Learning Compared to Other Machine Learning Approaches Applications of Deep Learning How to run the Neural Network using TensorFlow Cases of Study with Real Data Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/