The The Reinforcement Learning Workshop
DOWNLOAD
Download The The Reinforcement Learning Workshop PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The The Reinforcement Learning Workshop book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
The The Reinforcement Learning Workshop
DOWNLOAD
Author : Alessandro Palmas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-08-18
The The Reinforcement Learning Workshop written by Alessandro Palmas 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 2020-08-18 with Computers categories.
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
The Reinforcement Learning Workshop
DOWNLOAD
Author : Alessandro Palmas
language : en
Publisher:
Release Date : 2020
The Reinforcement Learning Workshop written by Alessandro Palmas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Algorithms categories.
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key Features Use TensorFlow to write reinforcement learning agents for performing challenging tasks Learn how to solve finite Markov decision problems Train models to understand popular video games like Breakout Book Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learn Use OpenAI Gym as a framework to implement RL environments Find out how to define and implement reward function Explore Markov chain, Markov decision process, and the Bellman equation Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning Understand the multi-armed bandit problem and explore various strategies to solve it Build a deep Q model network for playing the video game Breakout Who this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
The The Machine Learning Workshop
DOWNLOAD
Author : Hyatt Saleh
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-22
The The Machine Learning Workshop written by Hyatt Saleh 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 2020-07-22 with Computers categories.
Take a comprehensive and step-by-step approach to understanding machine learning Key FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook Description Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you’ll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms. What you will learnUnderstand how to select an algorithm that best fits your dataset and desired outcomeExplore popular real-world algorithms such as K-means, Mean-Shift, and DBSCANDiscover different approaches to solve machine learning classification problemsDevelop neural network structures using the scikit-learn packageUse the NN algorithm to create models for predicting future outcomesPerform error analysis to improve your model's performanceWho this book is for The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.
Deep Reinforcement Learning
DOWNLOAD
Author : Aske Plaat
language : en
Publisher: Springer Nature
Release Date : 2022-06-10
Deep Reinforcement Learning written by Aske Plaat and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-10 with Computers categories.
Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
Proceedings Of The Sixteenth Annual Conference Of The Cognitive Science Society
DOWNLOAD
Author : Ashwin Ram
language : en
Publisher: Routledge
Release Date : 2019-05-23
Proceedings Of The Sixteenth Annual Conference Of The Cognitive Science Society written by Ashwin Ram and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-23 with Psychology categories.
This volume features the complete text of all regular papers, posters, and summaries of symposia presented at the 16th annual meeting of the Cognitive Science Society.
Artificial Neural Networks And Machine Learning Icann 2019 Workshop And Special Sessions
DOWNLOAD
Author : Igor V. Tetko
language : en
Publisher: Springer Nature
Release Date : 2019-09-10
Artificial Neural Networks And Machine Learning Icann 2019 Workshop And Special Sessions written by Igor V. Tetko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Biologically Inspired Cognitive Architectures 2024
DOWNLOAD
Author : Alexei V. Samsonovich
language : en
Publisher: Springer Nature
Release Date : 2024-12-09
Biologically Inspired Cognitive Architectures 2024 written by Alexei V. Samsonovich and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-09 with Computers categories.
This book reports on original approaches intended to support the development of biologically inspired cognitive architectures. It bridges together different disciplines, including artificial intelligence, linguistics, neuro- and social sciences, psychology and philosophy of mind, among others. The chapters are based on contributions presented at the 2024 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence (the 15th Annual Meeting of the BICA Society, BICA*AI 2024), organized in collaboration with the 17th Conference on Artificial General Intelligence (AGI 2024) and held on August 13-16, 2024, in Seattle, WA, USA. They cover emerging methods, theories and ideas towards the realization of general-purpose humanlike artificial intelligence or fostering a better understanding of the ways the human mind works. All in all, this book provides engineers, mathematicians, psychologists, computer scientists and other experts with a timely snapshot of recent research and a source of inspiration for future developments in the broadly intended areas of artificial intelligence and biological inspiration.
Discovery Science
DOWNLOAD
Author : Steffen Lange
language : en
Publisher: Springer
Release Date : 2003-08-03
Discovery Science written by Steffen Lange and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-03 with Computers categories.
This volume contains the papers presented at the 5th International Conference on Discovery Science (DS 2002) held at the Mövenpick Hotel, Lub ̈eck, G- many, November 24-26, 2002. The conference was supported by CorpoBase, DFKI GmbH, and JessenLenz. The conference was collocated with the 13th International Conference on - gorithmic Learning Theory (ALT 2002). Both conferences were held in parallel and shared?ve invited talks as well as all social events. The combination of ALT 2002 and DS 2002 allowed for a comprehensive treatment of recent de- lopments in computational learning theory and machine learning - some of the cornerstones of discovery science. In response to the call for papers 76 submissions were received. The program committee selected 17 submissions as regular papers and 29 submissions as poster presentations of which 27 have been submitted for publication. This selection was based on clarity, signi?cance, and originality, as well as on relevance to the rapidly evolving?eld of discovery science.
Machine Learning Proceedings 1989
DOWNLOAD
Author : Alberto Maria Segre
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28
Machine Learning Proceedings 1989 written by Alberto Maria Segre and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Machine Learning Proceedings 1989
Agents And Artificial Intelligence
DOWNLOAD
Author : Jaap van den Herik
language : en
Publisher: Springer
Release Date : 2018-12-30
Agents And Artificial Intelligence written by Jaap van den Herik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-30 with Computers categories.
This book contains the revised and extended versions of selected papers from the 10th International Conference, ICAART 2018, held in Funchal, Madeira, Portugal, in January 2018. The 45 full papers together with 42 short papers and 26 Posters were carefully reviewed and selected from 161 initial submissions. The papers are organized in topics such as Agents, Artificial Intelligence, Semantic Web, Multi-Agent Systems, Distributed Problem Solving, Agent Communication and much more.