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Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also ...
Coefficient of Variation and Machine Learning Applications
Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.
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92.99 USD

Coefficient of Variation and Machine Learning Applications

by C. Raghavendra Rao, Nilanjan Dey, Raghava Morusupalli, K. Hima Bindu
Hardback
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Information retrieval (IR) is considered to be the science of searching for information from a variety of information sources related to texts, images, sounds, or multimedia. With the rise of the internet and digital databases, updated information retrieval methodologies are essential to ensure the continued facilitation and enhancement of information ...
Critical Approaches to Information Retrieval Research
Information retrieval (IR) is considered to be the science of searching for information from a variety of information sources related to texts, images, sounds, or multimedia. With the rise of the internet and digital databases, updated information retrieval methodologies are essential to ensure the continued facilitation and enhancement of information exchange. Critical Approaches to Information Retrieval Research is a critical scholarly publication that provides multidisciplinary examinations of theoretical innovations and methods in information retrieval technologies including search and storage applications for data, text, image, sound, document, and video retrieval. Featuring a wide range of topics including data mining, machine learning, and ontology, this book is ideal for librarians, software engineers, data scientists, professionals, researchers, information engineers, scientists, practitioners, and academicians working in the fields of computer science, information technology, information and communication sciences, education, health, library, and more.
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204.750000 USD

Critical Approaches to Information Retrieval Research

Hardback
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The development of software has expanded substantially in recent years. As these technologies continue to advance, well-known organizations have begun implementing these programs into the ways they conduct business. These large companies play a vital role in the economic environment, so understanding the software that they utilize is pertinent in ...
Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities
The development of software has expanded substantially in recent years. As these technologies continue to advance, well-known organizations have begun implementing these programs into the ways they conduct business. These large companies play a vital role in the economic environment, so understanding the software that they utilize is pertinent in many aspects. Researching and analyzing the tools that these corporations use will assist in the practice of software engineering and give other organizations an outline of how to successfully implement their own computational methods. Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities is an essential reference source that discusses advanced software methods that prominent companies have adopted to develop high quality products. This book will examine the various devices that organizations such as Google, Cisco, and Facebook have implemented into their production and development processes. Featuring research on topics such as database management, quality assurance, and machine learning, this book is ideally designed for software engineers, data scientists, developers, programmers, professors, researchers, and students seeking coverage on the advancement of software devices in today's major corporations.
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204.750000 USD

Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities

Hardback
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As today's world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer ...
Handbook of Research on Emerging Trends and Applications of Machine Learning
As today's world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
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393.750000 USD

Handbook of Research on Emerging Trends and Applications of Machine Learning

Hardback
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This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book ...
Data Science and Machine Learning: Mathematical and Statistical Methods
This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey! -Nicholas Hoell, University of Toronto This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. Presentation is self-contained, accessible, and comprehensive. Extensive list of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout. The Authors: Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method-an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences. Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley). Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.
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139.46 USD

Data Science and Machine Learning: Mathematical and Statistical Methods

by Radislav Vaisman, Thomas Taimre, Zdravko Botev, Dirk P. Kroese
Hardback
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This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning ...
Text Mining with Machine Learning: Principles and Techniques
This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.
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209.950000 USD

Text Mining with Machine Learning: Principles and Techniques

by Arnost Svoboda, Frantisek Darena, Jan Zizka
Hardback
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Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and ...
A Primer on Machine Learning Applications in Civil Engineering
Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB (R) exercises
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185.97 USD

A Primer on Machine Learning Applications in Civil Engineering

by Paresh Chandra Deka
Hardback
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Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning ...
Practical Deep Learning for Cloud and Mobile: Real-World AI & Computer Vision Projects Using Python, Keras & TensorFlow
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's Not Hotdog, image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology
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119.01 USD

Practical Deep Learning for Cloud and Mobile: Real-World AI & Computer Vision Projects Using Python, Keras & TensorFlow

by Meher Kasam, Siddha Ganju, Anirudh Koul
Paperback / softback
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Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and ...
Practical Time Series Analysis: Prediction with Statistics and Machine Learning
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You'll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
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104.13 USD

Practical Time Series Analysis: Prediction with Statistics and Machine Learning

by Aileen Nielsen
Paperback / softback
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Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Taking a task-based approach, you'll learn how to build features that use powerful ...
Practical Artificial Intelligence with Swift: From Fundamental Theory to Development of AI-Driven Apps
Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Taking a task-based approach, you'll learn how to build features that use powerful AI features to identify images, make predictions, generate content, recommend things, and more. AI is increasingly essential for every developer-and you don't need to be a data scientist or mathematician to take advantage of it in your apps. Explore Swift-based AI and ML techniques for building applications. Learn where and how AI-driven features make sense. Inspect tools such as Apple's Python-powered Turi Create and Google's Swift for TensorFlow to train and build models. I: Fundamentals and Tools-Learn AI basics, our task-based approach, and discover how to build or find a dataset. II: Task Based AI-Build vision, audio, text, motion, and augmentation-related features; learn how to convert preexisting models. III: Beyond-Discover the theory behind task-based practice, explore AI and ML methods, and learn how you can build it all from scratch... if you want to
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89.25 USD

Practical Artificial Intelligence with Swift: From Fundamental Theory to Development of AI-Driven Apps

by Tim Nugent, Paris Buttfield-Addison, Jonathon Manning, Mars Geldard
Paperback / softback
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Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key Features Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models Implement RL algorithms to solve control and optimization challenges faced by data scientists today Apply modern RL libraries to simulate a controlled ...
PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python
Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key Features Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models Implement RL algorithms to solve control and optimization challenges faced by data scientists today Apply modern RL libraries to simulate a controlled environment for your projects Book DescriptionReinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learn Use Q-learning and the state-action-reward-state-action (SARSA) algorithm to solve various Gridworld problems Develop a multi-armed bandit algorithm to optimize display advertising Scale up learning and control processes using Deep Q-Networks Simulate Markov Decision Processes, OpenAI Gym environments, and other common control problems Select and build RL models, evaluate their performance, and optimize and deploy them Use policy gradient methods to solve continuous RL problems Who this book is forMachine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.
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41.990000 USD

PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python

by Yuxi (Hayden) Liu
Paperback / softback
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There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art ...
Big Data in Multimodal Medical Imaging
There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.
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288.25 USD

Big Data in Multimodal Medical Imaging

Hardback
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This book reflects more than three decades of research on Cellular Automata (CA), and nearly a decade of work on the application of CA to model biological strings, which forms the foundation of 'A New Kind of Computational Biology' pioneered by the start-up, CARLBio. After a brief introduction on Cellular ...
A New Kind of Computational Biology: Cellular Automata Based Models for Genomics and Proteomics
This book reflects more than three decades of research on Cellular Automata (CA), and nearly a decade of work on the application of CA to model biological strings, which forms the foundation of 'A New Kind of Computational Biology' pioneered by the start-up, CARLBio. After a brief introduction on Cellular Automata (CA) theory and functional biology, it reports on the modeling of basic biological strings with CA, starting with the basic nucleotides leading to codon and anti-codon CA models. It derives a more involved CA model of DNA, RNA, the entire translation process for amino acid formation and the evolution of protein to its unique structure and function. In subsequent chapters the interaction of Proteins with other bio-molecules is also modeled. The only prior knowledge assumed necessary is an undergraduate knowledge of computer programming and biology. The book adopts a hands-on, do-it-yourself approach to enable readers to apply the method provided to derive the CA rules and comprehend how these are related to the physical 'rules' observed in biology. In a single framework, the authors have presented two branches of science - Computation and Biology. Instead of rigorous molecular dynamics modeling, which the authors describe as a Bottoms-Up model, or relying on the Top-Down new age Artificial Intelligence (AI) and Machine Language (ML) that depends on extensive availability of quality data, this book takes the best from both the Top-Down and Bottoms-up approaches and establishes how the behavior of complex molecules is represented in CA. The CA rules are derived from the basic knowledge of molecular interaction and construction observed in biological world but mapped to a few subset of known results to derive and predict results. This book is useful for students, researchers and industry practitioners who want to explore modeling and simulation of the physical world complex systems from a different perspective. It raises the inevitable the question - 'Are life and the universe nothing but a collection of continuous systems processing information'.
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83.990000 USD

A New Kind of Computational Biology: Cellular Automata Based Models for Genomics and Proteomics

by Somshubhro Pal Choudhury, Adip Dutta, Soumyabrata Ghosh, Parimal Pal Chaudhuri
Paperback / softback
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Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Key Features Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks Understand and develop model-free and model-based algorithms for building self-learning agents Work with advanced Reinforcement Learning concepts and algorithms such ...
Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges
Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Key Features Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks Understand and develop model-free and model-based algorithms for building self-learning agents Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies Book DescriptionReinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS. By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community. What you will learn Develop an agent to play CartPole using the OpenAI Gym interface Discover the model-based reinforcement learning paradigm Solve the Frozen Lake problem with dynamic programming Explore Q-learning and SARSA with a view to playing a taxi game Apply Deep Q-Networks (DQNs) to Atari games using Gym Study policy gradient algorithms, including Actor-Critic and REINFORCE Understand and apply PPO and TRPO in continuous locomotion environments Get to grips with evolution strategies for solving the lunar lander problem Who this book is forIf you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You'll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.
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36.740000 USD

Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges

by Andrea Lonza
Paperback / softback
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Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark--a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much ...
Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics
Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark--a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code. Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
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52.500000 USD

Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics

by Michael Bowles
Paperback / softback
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Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very ...
Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep learning models to production Explore PyTorch use cases from several leading companies Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia Use PyTorch's torchaudio library to classify audio data with a convolutional-based model Debug PyTorch models using TensorBoard and flame graphs Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
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52.490000 USD

Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

by Ian Pointer
Paperback / softback
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The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and ...
Computer Vision and Image Processing: Fundamentals and Applications
The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and feature selection for pattern classification/recognition, and advanced concepts like object classification, object tracking, image-based rendering, and image registration. Intended to be a companion to a typical teaching course on computer vision, the book takes a problem-solving approach.
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146.950000 USD

Computer Vision and Image Processing: Fundamentals and Applications

by Manas Kamal Bhuyan
Paperback / softback
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Interdisciplinary approaches using Machine Learning and Deep Learning techniques are smartly addressing real life challenges and have emerged as an inseparable element of disruption in current times. Applications of Disruptive Technology in Management practices are an ever interesting domain for researchers and professionals. This volume entitled Emerging Trends in Disruptive ...
Emerging Trends in Disruptive Technology Management for Sustainable Development
Interdisciplinary approaches using Machine Learning and Deep Learning techniques are smartly addressing real life challenges and have emerged as an inseparable element of disruption in current times. Applications of Disruptive Technology in Management practices are an ever interesting domain for researchers and professionals. This volume entitled Emerging Trends in Disruptive Technology Management for Sustainable Development has attempted to collate five different interesting research approaches that have innovatively reflected diverse potential of disruptive trends in the era of 4th. Industrial Revolution. The uniqueness of the volume is going to cater the entrepreneurs and professionals in the domain of artificial intelligence, machine learning, deep learning etc. with its unique propositions in each of the chapters. The volume is surely going to be a significant source of knowledge and inspiration to those aspiring minds endeavouring to shape their futures in the area of applied research in machine learning and computer vision. The expertise and experiences of the contributing authors to this volume is encompassing different fields of proficiencies. This has set an excellent prelude to discover the correlation among multidisciplinary approaches of innovation. Covering a broad range of topics initiating from IoT based sustainable development to crowd sourcing concepts with a blend of applied machine learning approaches has made this volume a must read to inquisitive wits. Features Assorted approaches to interdisciplinary research using disruptive trends Focus on application of disruptive technology in technology management Focus on role of disruptive technology on sustainable development Promoting green IT with disruptive technology The book is meant to benefit several categories of students and researchers. At the students' level, this book can serve as a treatise/reference book for the special papers at the masters level aimed at inspiring possibly future researchers. Newly inducted PhD aspirants would also find the contents of this book useful as far as their compulsory course-works are concerned. At the researchers' level, those interested in interdisciplinary research would also be benefited from the book. After all, the enriched interdisciplinary contents of the book would always be a subject of interest to the faculties, existing research communities and new research aspirants from diverse disciplines of the concerned departments of premier institutes across the globe. This is expected to bring different research backgrounds (due to its cross platform characteristics) close to one another to form effective research groups all over the world. Above all, availability of the book should be ensured to as much universities and research institutes as possible through whatever graceful means it may be. Hope this volume will cater as a ready reference to your quest for diving deep into the ocean of technology management for 4th. Industrial Revolution.
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92.99 USD

Emerging Trends in Disruptive Technology Management for Sustainable Development

Hardback
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Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially ...
Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers
Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients' acute and chronic condition loads and demographic characteristics
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251.06 USD

Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers

by Sanjay Ranka, Elizabeth Shenkman, Chris Delcher, Chengliang Yang
Hardback
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The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area ...
Data Science: Theory, Analysis and Applications
The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: * Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. * Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. * Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.
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193.41 USD

Data Science: Theory, Analysis and Applications

Hardback
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Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services Design robust architectures to ...
Hands-On Artificial Intelligence on Amazon Web Services: Decrease the time to market for AI and ML applications with the power of AWS
Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book DescriptionFrom data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to solve real-world problems. You'll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You'll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you'll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you'll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn Gain useful insights into different machine and deep learning models Build and deploy robust deep learning systems to production Train machine and deep learning models with diverse infrastructure specifications Scale AI apps without dealing with the complexity of managing the underlying infrastructure Monitor and Manage AI experiments efficiently Create AI apps using AWS pre-trained AI services Who this book is forThis book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.
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47.240000 USD

Hands-On Artificial Intelligence on Amazon Web Services: Decrease the time to market for AI and ML applications with the power of AWS

by Charles Song, Subhashini Tripuraneni
Paperback / softback
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In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety ...
Designing Robot Behavior in Human-Robot Interactions
In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety during human-robot interactions, efficiency in real-time robot motion planning, imitation of human behaviors from demonstration, dexterity of robots to adapt to different environments and tasks, cooperation among robots and humans with conflict resolution. These methods are applied in various scenarios, such as human-robot collaborative assembly, robot skill learning from human demonstration, interaction between autonomous and human-driven vehicles, etc. Key Features: Proposes a unified framework to model and analyze human-robot interactions under different modes of interactions. Systematically discusses the control, decision and learning algorithms to enable robots to interact safely with humans in a variety of applications. Presents numerous experimental studies with both industrial collaborative robot arms and autonomous vehicles.
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199.450000 USD

Designing Robot Behavior in Human-Robot Interactions

by Masayoshi Tomizuka, Hsien-Chung Lin, Te Tang, Changliu Liu
Hardback
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Hybrid computational intelligent techniques are efficient in dealing with the real-world problems encountered in engineering fields. The primary objective of this book is to provide an exhaustive introduction as well as review of the hybrid computational intelligent paradigm, with supportive case studies. In addition, it aims to provide a gallery ...
Hybrid Computational Intelligence: Research and Applications
Hybrid computational intelligent techniques are efficient in dealing with the real-world problems encountered in engineering fields. The primary objective of this book is to provide an exhaustive introduction as well as review of the hybrid computational intelligent paradigm, with supportive case studies. In addition, it aims to provide a gallery of engineering applications where this computing paradigm can be effectively use. Finally, it focuses on the recent quantum inspired hybrid intelligence to develop intelligent solutions for the future. The book also incorporates video demonstrations of each application for better understanding of the subject matter.
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260.36 USD

Hybrid Computational Intelligence: Research and Applications

Hardback
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With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You'll start with deep learning basics and move quickly to the ...
Deep Learning from Scratch: Building with Python from First Principles
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you'll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models-accompanied by working code examples and mathematical explanations-for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework
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62.990000 USD

Deep Learning from Scratch: Building with Python from First Principles

by Seth Weidman
Paperback / softback
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A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0 Key Features Understand the basics of machine learning and discover the power of neural networks and deep learning Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0 Solve any deep learning problem by ...
Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks
A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0 Key Features Understand the basics of machine learning and discover the power of neural networks and deep learning Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0 Solve any deep learning problem by developing neural network-based solutions using TF 2.0 Book DescriptionTensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you'll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production. What you will learn Grasp machine learning and neural network techniques to solve challenging tasks Apply the new features of TF 2.0 to speed up development Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines Perform transfer learning and fine-tuning with TensorFlow Hub Define and train networks to solve object detection and semantic segmentation problems Train Generative Adversarial Networks (GANs) to generate images and data distributions Use the SavedModel file format to put a model, or a generic computational graph, into production Who this book is forIf you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful. Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.
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41.990000 USD

Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

by Paolo Galeone
Paperback / softback
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This book provides theoretical and practical knowledge on AI and swarm intelligence. It provides a methodology for EA (evolutionary algorithm)-based approach for complex adaptive systems with the integration of several meta-heuristics, e.g., ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), and PSO (Particle Swarm Optimization), etc. These developments contribute towards ...
AI and SWARM: Evolutionary Approach to Emergent Intelligence
This book provides theoretical and practical knowledge on AI and swarm intelligence. It provides a methodology for EA (evolutionary algorithm)-based approach for complex adaptive systems with the integration of several meta-heuristics, e.g., ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), and PSO (Particle Swarm Optimization), etc. These developments contribute towards better problem-solving methodologies in AI. The book also covers emerging uses of swarm intelligence in applications such as complex adaptive systems, reaction-diffusion computing, and diffusion-limited aggregation, etc. Another emphasis is its real-world applications. We give empirical examples from real-world problems and show that the proposed approaches are successful when addressing tasks from such areas as swarm robotics, silicon traffics, image understanding, Vornoi diagrams, queuing theory, and slime intelligence, etc. Each chapter begins with the background of the problem followed by the current state-of-the-art techniques of the field, and ends with a detailed discussion. In addition, the simulators, based on optimizers such as PSO and ABC complex adaptive system simulation, are described in detail. These simulators, as well as some source codes, are available online on the author's website for the benefit of readers interested in getting some hands-on experience of the subject. The concepts presented in this book aim to promote and facilitate the effective research in swarm intelligence approaches in both theory and practice. This book would also be of value to other readers because it covers interdisciplinary research topics that encompass problem-solving tasks in AI, complex adaptive systems, and meta-heuristics.
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188.950000 USD

AI and SWARM: Evolutionary Approach to Emergent Intelligence

by Hitoshi Iba
Hardback
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With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal ...
Machine Learning Pocket Reference: Working with Structured Data in Python
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
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26.240000 USD

Machine Learning Pocket Reference: Working with Structured Data in Python

by Matt Harrison
Paperback / softback
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The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation ...
Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques
The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
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206.42 USD

Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques

Hardback
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Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers ...
Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition
Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. - Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building - Discover common neural network frameworks with Amazon SageMaker - Solve computer vision problems with Amazon Rekognition - Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
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52.500000 USD

Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition

by Abhishek Mishra
Paperback / softback
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Mathematics of Quantum Computing: An Introduction
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146.990000 USD

Mathematics of Quantum Computing: An Introduction

by Wolfgang Scherer
Hardback
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