The The Reinforcement Learning Workshop
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The The Reinforcement Learning Workshop
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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
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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 Deep Learning Workshop
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Author : Mirza Rahim Baig
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31
The Deep Learning Workshop written by Mirza Rahim Baig 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 2020-07-31 with Computers categories.
Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized images Who this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.
Machine Learning Workshop
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Author : HYATT. SALEH
language : en
Publisher:
Release Date : 2020
Machine Learning Workshop written by HYATT. SALEH and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The The Machine Learning Workshop
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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.
Recent Advances In Reinforcement Learning
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Author : Sertan Girgin
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12
Recent Advances In Reinforcement Learning written by Sertan Girgin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12 with Computers categories.
This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008. The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.
The Unsupervised Learning Workshop
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Author : Aaron Jones
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-29
The Unsupervised Learning Workshop written by Aaron Jones 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 2020-07-29 with Computers categories.
Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities Key FeaturesGet familiar with the ecosystem of unsupervised algorithmsLearn interesting methods to simplify large amounts of unorganized dataTackle real-world challenges, such as estimating the population density of a geographical areaBook Description Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights. What you will learnDistinguish between hierarchical clustering and the k-means algorithmUnderstand the process of finding clusters in dataGrasp interesting techniques to reduce the size of dataUse autoencoders to decode dataExtract text from a large collection of documents using topic modelingCreate a bag-of-words model using the CountVectorizerWho this book is for If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.
Recent Advances In Reinforcement Learning
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Author : Scott Sanner
language : en
Publisher: Springer
Release Date : 2012-05-19
Recent Advances In Reinforcement Learning written by Scott Sanner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-19 with Computers categories.
This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
Machine Learning Proceedings 1992
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Author : Peter Edwards
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28
Machine Learning Proceedings 1992 written by Peter Edwards 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 1992
Artificial Intelligence Ijcai 2019 International Workshops
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Author : Amal El Fallah Seghrouchni
language : en
Publisher: Springer Nature
Release Date : 2020-08-17
Artificial Intelligence Ijcai 2019 International Workshops written by Amal El Fallah Seghrouchni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-17 with Computers categories.
This book presents selected papers of 12 Workshops held in conjunction with the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, in Macao, China, in August 2019. The workshops included in this volume are: AI4KM 2019: 7th International Workshop on Artificial Intelligence for Knowledge Management and Innovation. FinNLP 2019: First International Workshop on Financial Technology and Natural Language Processing. OR 2019: 32nd International Workshop on Qualitative Reasoning. SURL 2019: Second International Workshop on Scaling-Up Reinforcement Learning. First International Workshop on Bringing Semantic Knowledge into Vision and Text Understanding. EASyHAT 2019: First International Workshop on Evaluation of Adaptive Systems for Human-Autonomy Teaming. ACAN 2019: 12th International Workshop on Agent-based Complex Automated Negotiations. First International Workshop on Deep Learning for Human Activity Recognition. HAI 2019: Second International Workshop on Humanizing AI. International Workshop on Language Sense on Computer. AISafety 2019: International Workshop on Artificial Intelligence Safety. DeLBP 2019: 4th International Workshop on Declarative Learning Based Programming.