Hands-On Markov Models with Python

Hands-On Markov Models with Python

Author: Ankur Ankan

Publisher: Packt Publishing Ltd

Published: 2018-09-27

Total Pages: 172

ISBN-13: 1788629337

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Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook Description Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learnExplore a balance of both theoretical and practical aspects of HMMImplement HMMs using different datasets in Python using different packagesUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference in HMMsImplement HMMs in finance, natural language processing (NLP), and image processingDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithmWho this book is for Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book


Book Synopsis Hands-On Markov Models with Python by : Ankur Ankan

Download or read book Hands-On Markov Models with Python written by Ankur Ankan and published by Packt Publishing Ltd. This book was released on 2018-09-27 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook Description Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learnExplore a balance of both theoretical and practical aspects of HMMImplement HMMs using different datasets in Python using different packagesUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference in HMMsImplement HMMs in finance, natural language processing (NLP), and image processingDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithmWho this book is for Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book

Markov Models

Markov Models

Author: Robert Wilson

Publisher: Createspace Independent Publishing Platform

Published: 2017-06-10

Total Pages: 150

ISBN-13: 9781548002206

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Do you want to become a data science Savvy? If reading about Markov models, stochastic processes, and probabilities leaves you scratching your head, then you have definitely come to the right place. If you are looking for the most no-nonsense guide that will keep you on the right course during the turbulent ride filled with scientific enigmas, machine learning, and predicting probabilities of hidden, unobservable states, then you have found your perfect companion. This book will Cover: What is Markov models How to make predictions with Markov Models How to learn without supervision How do Markov Models use prediction? Hidden Markov Models and how to use them The secrets of Markov Chains Tips and tricks on how to use Markov Models and machine learning Markov Models with Python Markov Models Examples and predictions How to build and implement HMM algorithms How to use Markov Models to master machine learning The secrets of Supervised and unsupervised machine learning The three components of Hidden Markov Models And much, much more! By the end of this book, I guarantee that you will dive easily into the data science world. Save yourself the hard work and frustration by downloading this book today. Download your free copy today (Kindle Unlimited only)


Book Synopsis Markov Models by : Robert Wilson

Download or read book Markov Models written by Robert Wilson and published by Createspace Independent Publishing Platform. This book was released on 2017-06-10 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to become a data science Savvy? If reading about Markov models, stochastic processes, and probabilities leaves you scratching your head, then you have definitely come to the right place. If you are looking for the most no-nonsense guide that will keep you on the right course during the turbulent ride filled with scientific enigmas, machine learning, and predicting probabilities of hidden, unobservable states, then you have found your perfect companion. This book will Cover: What is Markov models How to make predictions with Markov Models How to learn without supervision How do Markov Models use prediction? Hidden Markov Models and how to use them The secrets of Markov Chains Tips and tricks on how to use Markov Models and machine learning Markov Models with Python Markov Models Examples and predictions How to build and implement HMM algorithms How to use Markov Models to master machine learning The secrets of Supervised and unsupervised machine learning The three components of Hidden Markov Models And much, much more! By the end of this book, I guarantee that you will dive easily into the data science world. Save yourself the hard work and frustration by downloading this book today. Download your free copy today (Kindle Unlimited only)

Markov Models

Markov Models

Author: Robert Tier

Publisher: Createspace Independent Publishing Platform

Published: 2017-03-12

Total Pages: 66

ISBN-13: 9781544657288

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Discover How to Master Unsupervised Machine Learning and Crack Some of the Greatest Data Enigmas With Markov Models! Would you like to unlock the mysteries of Data Science? Are you yearning to understand how to make educated predictions on the weather, horse races, your unborn baby's facial features, or your boss's next black mood? Would you like a guide to explain these and many other "phenomenons" in clear, easy-to-understand language? If the answer is 'yes' then you'll want to Download this book today! It's never been easier to make predictions and smart analysis with the use of Markov Models. You don't need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills and a decent knowledge of Python programming in order to solve the most perplexing problems. And if you're unfamiliar with Python programming or Machine learning, don't worry, it'll all be explained in this book. Inside this book I'm going to show you how to be a data master. You'll discover how to solve almost-unsolvable machine learning problems in no time. I'm going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across. Download This Book Today and Discover: How to program with Python The secrets behind unsupervised machine learning How to use Markov Models to master machine learning How to make predictions with Markov Models How to use Markov Chains How to use Hidden Markov Models The 3 main problems of Markov Models and how to overcome them How to use Python to find the probability of longer and more complex problems What packages to get for using Python for Markov Models How to implement HMM algorithms How to build a speech recognizer A code that will turn gibberish into understandable text How to forecast the weather The secrets behind Queueing Theory The Markov Mutation Model The Secret Structure of Google's PageRank Algorithm How to perform Google PageRank in PythonAnd much, much more! So save yourself some time and frustration trying to learning these intricate algorithms on your own. Let me help you get started quickly and easily. Download Markov Models today and Enjoy Mastering Data Science!


Book Synopsis Markov Models by : Robert Tier

Download or read book Markov Models written by Robert Tier and published by Createspace Independent Publishing Platform. This book was released on 2017-03-12 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover How to Master Unsupervised Machine Learning and Crack Some of the Greatest Data Enigmas With Markov Models! Would you like to unlock the mysteries of Data Science? Are you yearning to understand how to make educated predictions on the weather, horse races, your unborn baby's facial features, or your boss's next black mood? Would you like a guide to explain these and many other "phenomenons" in clear, easy-to-understand language? If the answer is 'yes' then you'll want to Download this book today! It's never been easier to make predictions and smart analysis with the use of Markov Models. You don't need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills and a decent knowledge of Python programming in order to solve the most perplexing problems. And if you're unfamiliar with Python programming or Machine learning, don't worry, it'll all be explained in this book. Inside this book I'm going to show you how to be a data master. You'll discover how to solve almost-unsolvable machine learning problems in no time. I'm going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across. Download This Book Today and Discover: How to program with Python The secrets behind unsupervised machine learning How to use Markov Models to master machine learning How to make predictions with Markov Models How to use Markov Chains How to use Hidden Markov Models The 3 main problems of Markov Models and how to overcome them How to use Python to find the probability of longer and more complex problems What packages to get for using Python for Markov Models How to implement HMM algorithms How to build a speech recognizer A code that will turn gibberish into understandable text How to forecast the weather The secrets behind Queueing Theory The Markov Mutation Model The Secret Structure of Google's PageRank Algorithm How to perform Google PageRank in PythonAnd much, much more! So save yourself some time and frustration trying to learning these intricate algorithms on your own. Let me help you get started quickly and easily. Download Markov Models today and Enjoy Mastering Data Science!

Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python

Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python

Author: William Sullivan

Publisher: PublishDrive

Published: 2019-04-29

Total Pages: 73

ISBN-13:

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Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python o you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events. Extend your knowledge of machine learning, python programming & algorithms. What you'll Learn · Mathematics Behind Markov Algorithms · 3 Main Problems Of Markov Models And How To Overcome Them · Uses And Applications For Machine Learning · Python Programming · Speech Recognition · Weather Reporting · The Markov Rule And Markov's Model · Fundamental Axioms Of Statistics And Probability · Solutions · Theories · Artificial Intelligence · Bayesian Inference · Important Tools Used With HMM · And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov Models in python programming. This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price. What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!


Book Synopsis Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python by : William Sullivan

Download or read book Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python written by William Sullivan and published by PublishDrive. This book was released on 2019-04-29 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python o you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events. Extend your knowledge of machine learning, python programming & algorithms. What you'll Learn · Mathematics Behind Markov Algorithms · 3 Main Problems Of Markov Models And How To Overcome Them · Uses And Applications For Machine Learning · Python Programming · Speech Recognition · Weather Reporting · The Markov Rule And Markov's Model · Fundamental Axioms Of Statistics And Probability · Solutions · Theories · Artificial Intelligence · Bayesian Inference · Important Tools Used With HMM · And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov Models in python programming. This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price. What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!

Hands-On Simulation Modeling with Python

Hands-On Simulation Modeling with Python

Author: Giuseppe Ciaburro

Publisher: Packt Publishing Ltd

Published: 2020-07-17

Total Pages: 347

ISBN-13: 1838988653

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Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features Learn to create a digital prototype of a real model using hands-on examples Evaluate the performance and output of your prototype using simulation modeling techniques Understand various statistical and physical simulations to improve systems using Python Book Description Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learn Gain an overview of the different types of simulation models Get to grips with the concepts of randomness and data generation process Understand how to work with discrete and continuous distributions Work with Monte Carlo simulations to calculate a definite integral Find out how to simulate random walks using Markov chains Obtain robust estimates of confidence intervals and standard errors of population parameters Discover how to use optimization methods in real-life applications Run efficient simulations to analyze real-world systems Who this book is for Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.


Book Synopsis Hands-On Simulation Modeling with Python by : Giuseppe Ciaburro

Download or read book Hands-On Simulation Modeling with Python written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2020-07-17 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features Learn to create a digital prototype of a real model using hands-on examples Evaluate the performance and output of your prototype using simulation modeling techniques Understand various statistical and physical simulations to improve systems using Python Book Description Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learn Gain an overview of the different types of simulation models Get to grips with the concepts of randomness and data generation process Understand how to work with discrete and continuous distributions Work with Monte Carlo simulations to calculate a definite integral Find out how to simulate random walks using Markov chains Obtain robust estimates of confidence intervals and standard errors of population parameters Discover how to use optimization methods in real-life applications Run efficient simulations to analyze real-world systems Who this book is for Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.

Hands-On Simulation Modeling with Python

Hands-On Simulation Modeling with Python

Author: Giuseppe Ciaburro

Publisher: Packt Publishing Ltd

Published: 2022-11-30

Total Pages: 460

ISBN-13: 1804614467

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Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook Description Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that'll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you'll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you'll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is for This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.


Book Synopsis Hands-On Simulation Modeling with Python by : Giuseppe Ciaburro

Download or read book Hands-On Simulation Modeling with Python written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2022-11-30 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook Description Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that'll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you'll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you'll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is for This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.

Hands-On Q-Learning with Python

Hands-On Q-Learning with Python

Author: Nazia Habib

Publisher: Packt Publishing Ltd

Published: 2019-04-19

Total Pages: 200

ISBN-13: 1789345758

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Leverage the power of reward-based training for your deep learning models with Python Key FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook 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 libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree 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 learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho 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.


Book Synopsis Hands-On Q-Learning with Python by : Nazia Habib

Download or read book Hands-On Q-Learning with Python written by Nazia Habib and published by Packt Publishing Ltd. This book was released on 2019-04-19 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of reward-based training for your deep learning models with Python Key FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook 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 libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree 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 learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho 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.

Markov Models

Markov Models

Author: Duo Code

Publisher: Createspace Independent Publishing Platform

Published: 2017-05-29

Total Pages: 80

ISBN-13: 9781546999799

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Do you want to MASTER data science? Learn how MACHINE LEARNING systems can carry out multifaceted processes by learning from data? Understand MARKOV MODELS and how they can help your correctly forecast future events? Want to explore practical implementations of Markov models in PYTHON PROGRAMMING environment? Then you should DOWNLOAD your copy today The aim of machine learning is to train the computers or machine to learn on its own and make informed decisions in a relatively shorter time than what human beings can do. The primary objective of this book is to provide you with all the ins and outs of Markov models and unsupervised machine learning over a range of multi-faceted applications. Specifically, the book will explore practical implementations of Markov models in Python programming environment. You'll discover: - Types of machine learning algorithms - The mathematics behind markov algorithms - Application of markov models in python programming - Application of markov models in - gaming - Speech recognition - Weather reporting and much much more! DOWNLOAD YOUR COPY TODAY TO GAIN A HUGE ADVANTAGE OVER YOUR COMPETITORS


Book Synopsis Markov Models by : Duo Code

Download or read book Markov Models written by Duo Code and published by Createspace Independent Publishing Platform. This book was released on 2017-05-29 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to MASTER data science? Learn how MACHINE LEARNING systems can carry out multifaceted processes by learning from data? Understand MARKOV MODELS and how they can help your correctly forecast future events? Want to explore practical implementations of Markov models in PYTHON PROGRAMMING environment? Then you should DOWNLOAD your copy today The aim of machine learning is to train the computers or machine to learn on its own and make informed decisions in a relatively shorter time than what human beings can do. The primary objective of this book is to provide you with all the ins and outs of Markov models and unsupervised machine learning over a range of multi-faceted applications. Specifically, the book will explore practical implementations of Markov models in Python programming environment. You'll discover: - Types of machine learning algorithms - The mathematics behind markov algorithms - Application of markov models in python programming - Application of markov models in - gaming - Speech recognition - Weather reporting and much much more! DOWNLOAD YOUR COPY TODAY TO GAIN A HUGE ADVANTAGE OVER YOUR COMPETITORS

Markov Models Supervised and Unsupervised Machine Learning

Markov Models Supervised and Unsupervised Machine Learning

Author: William Sullivan

Publisher: Createspace Independent Publishing Platform

Published: 2017-09-03

Total Pages: 124

ISBN-13: 9781976050008

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Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook Do you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events Extend your knowledge of machine learning, python programming & algorithms What you'll Learn Mathematics Behind Markov Algorithms 3 Main Problems Of Markov Models And How To Overcome Them Uses And Applications For Machine Learning Python Programming Speech Recognition Weather Reporting The Markov Rule And Markov's Model Fundamental Axioms Of Statistics And Probability Solutions Theories Artificial Intelligence Bayesian Inference Important Tools Used With HMM And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level You will learn the ins and outs of machine learning, and its real world applications Also, specifically you will discover practical implementations of Markov Models in python programming This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!


Book Synopsis Markov Models Supervised and Unsupervised Machine Learning by : William Sullivan

Download or read book Markov Models Supervised and Unsupervised Machine Learning written by William Sullivan and published by Createspace Independent Publishing Platform. This book was released on 2017-09-03 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook Do you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events Extend your knowledge of machine learning, python programming & algorithms What you'll Learn Mathematics Behind Markov Algorithms 3 Main Problems Of Markov Models And How To Overcome Them Uses And Applications For Machine Learning Python Programming Speech Recognition Weather Reporting The Markov Rule And Markov's Model Fundamental Axioms Of Statistics And Probability Solutions Theories Artificial Intelligence Bayesian Inference Important Tools Used With HMM And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level You will learn the ins and outs of machine learning, and its real world applications Also, specifically you will discover practical implementations of Markov Models in python programming This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!

Hands-On Unsupervised Learning Using Python

Hands-On Unsupervised Learning Using Python

Author: Ankur A. Patel

Publisher: O'Reilly Media

Published: 2019-02-21

Total Pages: 362

ISBN-13: 1492035610

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Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks


Book Synopsis Hands-On Unsupervised Learning Using Python by : Ankur A. Patel

Download or read book Hands-On Unsupervised Learning Using Python written by Ankur A. Patel and published by O'Reilly Media. This book was released on 2019-02-21 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks