machine learning algorithms in depth vadim smolyakov

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

Updated to emphasize new features in Spark 2.4., this second edition shows data engineers and scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine-learning algorithms.

Objev podobné jako Learning Spark - Denny Lee, Brooke Wenig, Tathagata Das, Jules Damji

It s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you⠙re grokking as you go. You⠙ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.

Objev podobné jako Grokking Machine Learning - Luis Serrano

Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor s Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and WekaUnderstand decision trees, Bayesian networks, and artificial neural networksImplement Association Rule, Real Time, and Batch learningDevelop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.

Objev podobné jako Machine Learning - Jason Bell

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you ll find machine learningâ ”the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you ll dive into the tools, code, and math that make machine learning goâ ”and you ll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can t doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Objev podobné jako Machine Learning For Dummies - John Paul Mueller, Luca Massaron

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning â ”also known as data mining or data analyticsâ ” is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

Objev podobné jako Machine Learning for Business Analytics - Peter Gedeck, Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Inbal Yahav

This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.

Objev podobné jako Introduction to Machine Learning with Applications in Information Security - Mark Stamp

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning⠔also known as data mining or predictive analytics⠔is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learningA new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

Objev podobné jako Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Kuber R. Deokar

MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing usersâ ™ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. readers will also find: Updated material which improves the bookâ ™s usefulness as a reference for professionals beyond the classroomFour new chapters, covering topics including Text Mining and Responsible Data ScienceAn updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbookA guide to JMP Pro s new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

Objev podobné jako Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Muralidhara Anandamurthy, Mia L. Stephens

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompanimentÂ

Objev podobné jako Probabilistic Machine Learning - Kevin P. Murphy

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book s web site.

Objev podobné jako Mathematics for Machine Learning - A. Aldo Faisal, Marc Peter Deisenroth, Cheng Soon Ong

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Objev podobné jako Machine Learning with Neural Networks - Bernhard Mehlig

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Objev podobné jako Machine Learning Refined - Aggelos K. Katsaggelos, Reza Borhani, Jeremy Watt

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.

Objev podobné jako Introduction to Machine Learning with Python - Andreas C. Mueller

A friendly, fully-illustrated introduction to the most important computer programming algorithms. The algorithms you ll use most often as a programmer have already been discovered, tested, and proven. This book will prepare you for those pesky algorithms questions in every programming job interview and help you apply them in your day-to-day work. And if you want to understand them without slogging through dense multipage proofs, this is the book for you. In Grokking Algorithms, Second Edition you will discover: Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP complete and greedy algorithms Performance trade-offs between algorithms Exercises and code samples in every chapter Over 400 illustrations with detailed walkthroughs The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn t have to be complicated or boring! This new edition now includes fresh coverage of trees, NP complete problems, and code updates to Python 3. With easy-to-read, friendly explanations, clever examples, and exercises to sharpen your skills as you learn, youâ ™ll actually enjoy learning these important algorithms.

Objev podobné jako Grokking Algorithms - Aditya Bhargava

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics.Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout.New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exercises and 22 new problemsReader feedback–informed improvements to old problemsClearer, more personal, and gender-neutral writing styleColor added to improve visual presentationNotes, bibliography, and index updated to reflect developments in the fieldWebsite with new supplementary materialWarning: Avoid counterfeit copies of Introduction to Algorithms by buying only from reputable retailers. Counterfeit and pirated copies are incomplete and contain errors.

Objev podobné jako Introduction to Algorithms, fourth edition - Thomas H. Cormen, Charles E. Leiserson

AI is primed to revolutionize the way we build applications, offering exciting new ways to solve problems, uncover insights, innovate new products, and provide better user experiences. Successful AI is based on a set of core algorithms that form a base of knowledge shared by all data scientists. Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, readers learn the concepts, terminology, and theory they need to effectively incorporate AI algorithms into their applications. Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Donâ ™t worry if you arenâ ™t a calculus wunderkind; youâ ™ll need only the algebra you picked up in math class. â ¢ Use cases for different AI algorithms â ¢ How to encode problems and solutions using data structures â ¢ Intelligent search for game playing â ¢ Ant colony algorithms for path finding â ¢ Evolutionary algorithms for optimization problems For software developers with high school-level algebra and calculus skills.

Objev podobné jako Grokking Artificial Intelligence Algorithms - Rishal Hurbans

An accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas.Digital technology runs on algorithms, sets of instructions that describe how to do something efficiently. Application areas range from search engines to tournament scheduling, DNA sequencing, and machine learning. Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader. Louridas explains not just what algorithms are but also how they work, offering a wide range of examples and keeping mathematics to a minimum.After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century problems to today s social networks; searching, and how to find the fastest way to search; and sorting, and the importance of choosing the best algorithm for particular tasks. He then presents larger-scale applications: PageRank, Google s founding algorithm; and neural networks and deep learning. Finally, Louridas describes how all algorithms are nothing more than simple moves with pen and paper, and how from such a humble foundation rise all their spectacular achievements.

Objev podobné jako Algorithms - Panos Louridas

The leading introduction to computer algorithms in use today, including fifty algorithms every programmer should know Princeton Computer Science professors, Robert Sedgewick and Kevin Wayne, survey the most important computer algorithms in use and of interest to anyone working in science, mathematics, and engineering, and those who use computation in the liberal arts. They provide a full treatment of data structures and algorithms for key areas that enable you to confidently implement, debug, and put them to work in any computational environment. Fundamentals: Basic programming models Data abstraction Bags, queues, and stacks Analysis of algorithms Sorting Elementary sorts Mergesort Quicksort Priority queues Applications Graphs Undirected graphs Directed graphs Minimum spanning trees Shortest paths Strings String sorts Tries Substring search Regular expressions Data compression These algorithms are generally ingenious creations that, remarkably, can each be expressed in just a dozen or two lines of code. As a group, they represent problem-solving power of amazing scope. They have enabled the construction of computational artifacts, the solution of scientific problems, and the development of commercial applications that would not have been feasible without them.

Objev podobné jako Algorithms - Kevin Wayne, Robert Sedgewick

Written by more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets, and explores connections with data science and more traditional approaches. This is an invaluable resource for researchers and graduate students in financial engineering, as well as practitioners in the sector.

Objev podobné jako Machine Learning and Data Sciences for Financial Markets

Prepares students for Microsoft Exam 70-774-and helps them demonstrate real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT students ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.

Objev podobné jako Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning - Ginger Grant, Tamanaco Francisquez, Pau Sempere, Paco Gonzalez, Julio Granado

To create web designs that delight, entertain, and impress your users, you need to know CSS in depth! Getting goodâ ”really goodâ ”at CSS means learning everything that CSS is capable of. This totally revised new edition of CSS in Depth expands your CSS skills with the patterns, layouts, and methods you need to deliver truly beautiful front ends. In CSS in Depth, Second Edition you will learn how to: Create a web page with layout methods Develop essential website components, like dropdown menus and dialog boxes Make your website fully responsive across devices Organize your CSS for easy future maintenance Implement designer mockups with attention to detail Use animations to guide user focus Avoid common CSS pitfalls The more you know about CSS, the more confident you ll be at tackling any tricky website design! CSS in Depth has given thousands of web developers the tools and the inspiration to make sites that really pop. This second edition is packed with the latest best practices, new CSS language features, and essential advice on how to organize and maintain your CSS codebase. About the technology To deliver truly beautiful frontends, you need to know CSS inside and out. And as CSS grows and matures, even experienced CSS developers will find a whole new set of skills to catch up on! This guide will help you discover everything that CSS is capable ofâ ”from the must-knows and brand-new features to the hidden insights you won t find anywhere else!

Objev podobné jako CSS in Depth, Second Edition - Keith Grant

Humans learn best from feedbackâ ”we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques youâ ™ll need to implement it into your own projects. Key features â ¢ Structuring problems as Markov Decision Processes â ¢ Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them â ¢ Applying reinforcement learning algorithms to real-world problems Audience Youâ ™ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but thatâ ™s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.

Objev podobné jako Deep Reinforcement Learning in Action - Alexander Zai, Brandon Brown

Fully revised and updated to address how we understand kids with learning difficulties as well as changes in education today, this is the definitive guide to meeting the needs of students with learning difficulties in all classrooms.This third edition of Teaching Kids with Learning Difficulties in Today s Classroom provides information on integrated learning, problem solving, and critical thinking in line with Common Core State Standards and twenty-first-century skills. It reflects the use of technology and schoolwide cluster grouping in support of all students and includes proven, practical, classroom-tested strategies and step-by-step instructions for how to use them. Sidebars throughout highlight special information for working with students on the autism spectrum; tech tips describe technologies that are especially useful for kids with LD.Digital content includes all of the book s customizable forms and a PDF presentation for book study groups and professional development.

Objev podobné jako Teaching Kids with Learning Difficulties in Today

Kniha - autor Cathy O'Neil, 288 stran, anglicky, brožovaná bez přebalu matná With moral clarity and powerful storytelling, Cathy O'Neil reverse engineers the 'shame machine,' revealing its inner workings and inciting nothing short of a cultural reckoning that has the potential to blow this machine to bits' - Ruha BenjaminShame is being weaponized by governments and corporations to attack the most vulnerable. It's time to fight backShame is a powerful and sometimes useful tool. When we publicly shame corrupt politicians, abusive celebrities, or predatory corporations, we reinforce values of fairness and justice. But as best-selling author Cathy O'Neil argues in this revelatory book, shaming has taken a new and dangerous turn. It is increasingly being weaponized — used as a way to shift responsibility for social problems from institutions to individuals. Shaming children for not being able to afford school lunches or adults for not being able to find work lets...

Objev podobné jako The Shame Machine: Who Profits in the New Age of Humiliation (0593443381)

Focusing on the early philosophies of learning and key behavioural, cognitive, and social theorists, including Locke, Rousseau, Montessori, Piaget, Vygotsky, Bandura, Bronfenbrenner Bruner, this popular book provides a comprehensive overview of children⠲s learning. The authors highlight the strengths and weaknesses of each theoretical perspective, and encourage reflection on how different approaches impact on the learning environment. The discussion finishes with an exploration of the new sociology of childhood. New to this Second Edition are:·         a new chapter on ⠲What is theory and what is learning?·         a new chapter on ⠘The Changing nature of learning⠙There is also a new companion website which features:·         journal articles to read alongside each chapter·         podcasts from the authors explaining the key points on each topic ·         links to video material discussing key theories and methods.You can access the books online materials at study.sagepub.com/grayandmacblain2eAccessibly written, with key questions and recommended reading included, this book is essential for all those studying on child development, early childhood and childhood studies courses, and for anyone interested in understanding more about how children learn and think.Colette Gray is Head of Research Development and Principal Lecturer in Childhood Studies at Stanmillis University College, Belfast, and Sean MacBlain is Reader in Child Development and Disability at the University of St. Mark St. John, Plymouth.For access to the website

Objev podobné jako Learning Theories in Childhood - Sean MacBlain, Colette Gray

This latest book in the bestselling Mathematics Recovery® series gives mathematics educators a complete research-based framework for assessment, instruction and intervention in whole number arithmetic across grades K to 5. The integrated set of classroom tools includes: Nine carefully designed schedules of assessment tasks Nine models of learning progressions Ten teaching maps that guide the instructional progressions across key topics The book offers guidance on innovative video-based assessment, and an overview of principles of intervention instruction, giving you an integrated resource for supporting the children you teach. The Learning Framework in Number will be a useful guide for all primary and elementary school classroom teachers and assistants, and specialist teachers, including experienced Mathematics Recovery® instructors. The book will also be of significant interest to teacher educators and researchers.

Objev podobné jako The Learning Framework in Number - Robert J Wright, David Ellemor-Collins

It s not difficult to create something that will look like art; you just need to imitate an already existing genre or style. The challenge is to create something that will be able to trigger an art experience.” Magda Stanová studies where, in a spectrum of different kinds of experiences (jokes, magic tricks, pleasure from solving a mathematical or scientific problem), there are thrills triggered by art. All of these experiences are contingent upon a sufficient amount of novelty. The book contains a lot of drawings and diagrams.

Objev podobné jako Algorithms in Art - Magda Stanová

Written by a noted authority, this fully illustrated study describes and depicts the machine guns equipping Soviet and Russian troops after 1945. Following the USSR⠙s victory in World War II, the Soviet armed forces adopted a succession of new or improved machine guns. At squad level, the 7.62mm RPD and RP-46 light machine guns replaced the DPM, themselves being supplanted by the RPK from 1961. Firing the lighter 5.45×39mm cartridge, the RPK-74 was issued from 1974 and remains in use today. The 5.45mm RPK-16 entered Russian service in 2018. Having served alongside the 7.62mm PM M1910 Maxim during World War II, the 7.62mm SG-43 medium machine gun was updated as the SGM before being supplanted by the 7.62mm PK general-purpose machine gun, issued from 1961. The improved PKM made its debut in 1969 and still equips Russian troops today, being joined by the PKP in 2001 and the AEK-999 in 2008. First issued in 1938, the formidable 12.7mm DShK heavy machine gun remains in Russian service today as the DShkM. It was joined by the 14.5mm KPV from 1949, the 12.7mm NSV from 1971 and the 12.7mm Kord from 1998. In this illustrated survey, Leroy Thompson investigates the origins, development, combat use and legacy of all of these machine guns since 1945, from the start of the Cold War to the 2020s, casting light on their battlefield effectiveness and tactical influence.

Objev podobné jako Russian Machine Guns since 1945 - Leroy Thompson

OXY-TREAT Anti-Age In-Depth Oxygen Treatment, 1 ks, Pleťová séra pro ženy, Máte unavenou, matnou a povadlou pleť a chcete ji celkově revitalizovat? Sada intenzivní péče OXY-TREAT Anti-Age je špičkové dermokosmetické ošetření, které je vhodné pro domácí použití a kombinuje mimořádné regenerační vlastnosti aktivního kyslíku se specifickými složkami cílícími na povadlou pleť. Díky tomu intenzivní kúra podpoří omlazení pleti, bude stimulovat její metabolismus a působit proti jejímu stárnutí. Vlastnosti: sadu péče tvoří nbsp;gel kombinující kyslík s molekulami anti-age a tekutá emulze gel a tekutá emulze společně efektivně působí proti stárnutí pleti mají mimořádné regenerační účinky díky aktivnímu kyslíku stimulují růst a podporují vitalitu nových kožních buněk revitalizují a zlepšují stav unavené, matné a povadlé pleti efektivně bojují proti ztenčování pleti Výsledky testování: V rámci studie efektivity se testování zúčastnilo 30 subjektů pro řadu nbsp;OXY-TREAT Anti-Age nbsp;(210 testovaných subjektů celkem), péči (gel a tekutou emulzi) používaly 30 dní. 87 % z nbsp;30 subjektů se shodlo, že pleť vypadá po intenzivní kúře mladší 100 % souhlasilo, že intenzivní péče zanechává pleť zdravější 100 % potvrdilo, že péče zlepšila elasticitu pleti 100 % uvedlo, že jsou spokojeni s výsledným efektem a péči by doporučili Složení: karnosin, ubichinon, melatonin ‒ podporují omlazení pleti a stimulaci jejího metabolismu anti-age patent od Labo CH 700 nbsp;735, hydrolyzovaný sójový protein, extrakt z myrty obecné a teprenon ‒ pomáhají působit proti stárnutí pleti a oddalovat jej Jak aplikovat: Intenzivní péči proti stárnutí pleti OXY-TREAT Anti-Age je ideální používat v rámci 14denní intenzivní kúry. Při ní provádějte aplikaci na obličej 1× denně, nejlépe večer. Nejdříve na odlíčenou a suchou pleť rovnoměrně rozetřete 5‒6 dávek (cca 3 ml) hloubkového gelu, který kombinuje kyslík s aktivními molekulami anti-age. Ihned se vytvoří šumivá vrstva (jedná se o kyslík uvolňující se do pleti z molekul PFC). Nechte působit 8 minut, až do úplného vstřebání. Poté naneste tekutou emulzi (1 ml na jednu aplikaci na celý obličej). Notino tip: Pro maximální efekt doporučujeme používat sadu péče OXY-TREAT Anti-Age s denním/nočním krémem proti stárnutí pleti OXY-TREAT Anti-Age, nebo ji můžete kombinovat s denním/nočním krémem OXY-TREAT na sekundární problém.

Objev podobné jako OXY-TREAT Anti-Age In-Depth Oxygen Treatment intenzivní péče proti stárnutí pleti 1 ks

In this book, you ll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Objev podobné jako Designing Machine Learning Systems - Chip Huyen

How to put democracy at the heart of AI governanceArtificial intelligence and machine learning are reshaping our world. Police forces use them to decide where to send police officers, judges to decide whom to release on bail, welfare agencies to decide which children are at risk of abuse, and Facebook and Google to rank content and distribute ads. In these spheres, and many others, powerful prediction tools are changing how decisions are made, narrowing opportunities for the exercise of judgment, empathy, and creativity. In Algorithms for the People, Josh Simons flips the narrative about how we govern these technologies. Instead of examining the impact of technology on democracy, he explores how to put democracy at the heart of AI governance. Drawing on his experience as a research fellow at Harvard University, a visiting research scientist on Facebookâ ™s Responsible AI team, and a policy advisor to the UKâ ™s Labour Party, Simons gets under the hood of predictive technologies, offering an accessible account of how they work, why they matter, and how to regulate the institutions that build and use them. He argues that prediction is political: human choices about how to design and use predictive tools shape their effects. Approaching predictive technologies through the lens of political theory casts new light on how democracies should govern political choices made outside the sphere of representative politics. Showing the connection between technology regulation and democratic reform, Simons argues that we must go beyond conventional theorizing of AI ethics to wrestle with fundamental moral and political questions about how the governance of technology can support the flourishing of democracy.

Objev podobné jako Algorithms for the People - Josh Simons

With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.

Objev podobné jako Effective Machine Learning Teams - Ada Leung, David Tan, David Colls

Doc Brown s Owner s Workshop Manual, Discover the secrets of Doc Brown s time-traveling DeLorean with the first-ever under-the-bonnet user s manual featuring never-before-seen schematics and cutaways of cinema s most iconic car., One of the best-loved movie sagas of all time, the Back to the Future trilogy has left an indelible impact on popular culture. Back to the Future: DeLorean Time Machine: Owner s Workshop Manual delves into the secrets of the unique vehicle that transports Marty McFly and Doc Brown through time, including both the original version of the car and the updated flying model. From the DeLorean s unmistakable gull-wing doors to Doc s cutting-edge modifications, including the Flux Capacitor and Mr. Fusion, this manual offers unprecedented insight into the car s inner workings., Filled with exclusive illustrations and never-before-disclosed information, Back to the Future: DeLorean Time Machine: Owner s Workshop Manual is the perfect gift for the trilogy s legion of fans., Authors, Bob Gale is an Oscar-nominated screenwriter-producer-director best known as co-creator, co-writer, and co-producer of Back to the Future and its sequels. Gale was born and raised in St. Louis, Missouri, and graduated Phi Beta Kappa with a B.A. in Cinema from the University of Southern California in 1973. He has written over thirty screenplays, and his other film credits include 1941, I Wanna Hold Your Hand, Used Cars, Trespass, and Interstate 60. In addition to writing movies, Gale has written comic books including Spider-Man, Batman, and IDW s Back to the Future title, and has also served as an expert witness in over twenty-five plagiarism cases. Gale lives in Southern California with his wife and dog., Joe Walser combined decades of motion picture art department experience with his passion for Back to the Future to become the world s leading authority on the DeLorean time machine. In 2013, he led Universal Studios official restoration of the actual time machine vehicle used in all three Back to the Future movies, which is now on permanent display at the renowned Petersen Automotive Museum in Los Angeles. Walser has been directly involved in dozens of licensed Back to the Future products and projects, and has cocreated the world s largest Back to the Future fan celebrations, including the thirtieth anniversary We re Going Back event in 2015. He lives with his wife, daughter, and three sons in Los Angeles, California.

Objev podobné jako Back to the Future DeLorean Time Machine - Bob Gale

The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better. - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technologyMachine learning has made remarkable progress in recent years. We ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn t beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning⠔a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you ll have the knowledge and hands-on skills to apply deep learning in your own projects. what s insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generation about the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Objev podobné jako Deep Learning with Python - François Chollet

If you ve been curious about artificial intelligence and machine learning but didn t know where to start, this is the book you ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models performance. You ll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they re trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You ll conduct experiments along the way, building to a final case study that incorporates everything you ve learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

Objev podobné jako Practical Deep Learning, 2nd Edition - Ronald T. Kneusel

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. â ¢ Foundational reinforcement learning concepts and methods â ¢ The most popular deep reinforcement learning agents solving high-dimensional environments â ¢ Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.

Objev podobné jako Grokking Deep Reinforcement Learning - Miguel Morales

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field s key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning s relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson s wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Objev podobné jako Reinforcement Learning - Andrew G. Barto, Richard S. Sutton

NVIDIA s Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to successâ ”asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA s invention of the GPU sparked the PC gaming market. The company s pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Objev podobné jako Learning Deep Learning - Magnus Ekman

Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the ⠜brain⠝ behind the world⠙s smartest Artificial Intelligence systems out there. Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the ⠜black box⠝ API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Key Features:Build neural networks that can see and understand images Build an A.I. that will learn to defeat you in a classic Atari gameHands-on Learning Written for readers with high school-level math and intermediateprogramming skills. Experience with Calculus is helpful but notrequired. ABOUT THE TECHNOLOGY Deep Learning is a subset of Machine Learning, which is a field dedicated to the study and development of machines that can learn, often with the goal of eventually attaining general artificial intelligence.

Objev podobné jako Grokking Deep Learning - Andrew W Trask

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you ll discover just how effective and fun PyTorch can be. Key features â ¢ Using the PyTorch tensor API â ¢ Understanding automatic differentiation in PyTorch â ¢ Training deep neural networks â ¢ Monitoring training and visualizing results â ¢ Interoperability with NumPy Audience Written for developers with some knowledge of Python as well as basic linear algebra skills. Some understanding of deep learning will be helpful, however no experience with PyTorch or other deep learning frameworks is required. About the technology PyTorch is a machine learning framework with a strong focus on deep neural networks. Because it emphasizes GPU-based acceleration, PyTorch performs exceptionally well on readily-available hardware and scales easily to larger systems. Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch.

Objev podobné jako Deep Learning with PyTorch - Eli Stevens, Luca Antiga

Many of us have been desperately hurt and wounded in our lives. Torn between extreme opposites, we may feel ourselves to be flung and pulled in all directions, unaware that there can be a solution. The Transpersonal seeks to clarify the healing process. Itâ ™s a natural process, like the healing of a wound.

Objev podobné jako Journey in Depth

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Objev podobné jako An Introduction to Statistical Learning - Trevor Hastie, Robert Tibshirani, Daniela Witten, Gareth James

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARLÂ’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the fieldÂ’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers. First textbook to introduce the foundations and applications of MARL, written by experts in the fieldIntegrates reinforcement learning, deep learning, and game theoryPractical focus covers considerations for running experiments and describes environments for testing MARL algorithmsExplains complex concepts in clear and simple languageClassroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slidesÂ

Objev podobné jako Multi-Agent Reinforcement Learning - Filippos Christianos, Stefano V. Albrecht

Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, (NLP) expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning â ¢ Models for textual similarity â ¢ Deep memory-based NLP â ¢ Semantic role labeling â ¢ Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. Heâ ™s the technical coordinator of two large European Union-funded research security-related projects. Heâ ™s currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

Objev podobné jako Deep Learning for Natural Language Processing - Stephan Raaijmakers

Level: EYFS early years foundation stageSubject: MathsAn engaging Shapes, Colours and Patterns activity book to really help boost your childâ ™s progress at every stage of their learning!Fully in line with the Early Years Foundation Stage, this Maths book provides reassurance whilst supporting your childâ ™s learning at home.Combining useful Maths practice with engaging, colourful illustrations, this Shapes, Colours and Patterns practice book helps to boost your childâ ™s confidence and develop good learning habits for life. Each fun activity is designed to give your child a real sense of achievement.Included in this book:questions that allow children to practise the important skills learned at schoolcolourful activities that make learning fun and motivate children to learn at homehelpful tips and answers so that you can support your childâ ™s learning

Objev podobné jako Shapes, Colours and Patterns Ages 3-5 - Collins Easy Learning

Level: EYFSSubject: MathsAn engaging Maths activity book to really help boost your childâ ™s progress at every stage of their learning! Fully in line with the Early Years Foundation Stage, this book provides reassurance whilst supporting your childâ ™s learning at home.Combining useful practice with engaging, colourful illustrations, this Maths practice book helps to boost your childâ ™s confidence and develop good learning habits for life. Each fun activity is designed to give your child a real sense of achievement.Included in this book:questions that allow children to practise the important skills learned at schoolcolourful activities that make learning fun and motivate children to learn at homehelpful tips and answers so that you can support your childâ ™s learning

Objev podobné jako Maths Ages 3-5 - Collins Easy Learning

One of the seminal management books of the past 75 years, The Fifth Discipline is an international multi-million-copy bestseller. Written in an engaging and accessible way, with diagrams and illustrations, it will change the way you think and therefore way you and your team grows and develop. In the long run, the only sustainable source of competitive advantage is your organisation s ability to learn faster than its competitors.... Senge explains why the learning organization matters, provides an unvarnished summary of his management principals, offers some basic tools for practicing it, and shows what it s like to operate under this system. The book s concepts remain stimulating and relevant as ever -- Amazon.com 500 pages that I will no doubt keep coming back to -- ***** Reader review This is a book about growth, improvement and continuous development. If you wish to achieve these results for yourself, your home, or your organization, then you MUST read this -- ***** Reader review Has the power of revolutionizing your thinking on how to build organizations -- ***** Reader review Enlightening from start to finish -- ***** Reader review************************************************************************************************Peter Senge, founder and director of the Society for Organisational Learning and senior lecturer at MIT, has found the means of creating a learning organisation . In The Fifth Discipline, he draws the blueprints for an organisation where people expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning together.He fuses these features together into a coherent body of theory and practice, making the whole of an organisation more effective than the sum of its parts.Mastering the disciplines will:*Reignite the spark of learning, driven by people focused on what truly matters to them.*Bridge teamwork into macro-creativity.*Free you from confining assumptions and mind-sets.*Teach you to see the forest and the trees.*End the struggle between work and family time.The Fifth Discipline is a remarkable book that draws on science, spiritual values, psychology, the cutting edge of management thought and case studies of Senge s work with leading companies - reading it is a searching personal experience that guarantees a professional shift of mind.Written in an engaging and accessible way, with diagrams and illustrations, this publishing phenomenon is a must read for anyone interested in approaches to business growth, personal development and management coaching.

Objev podobné jako The Fifth Discipline: The art and practice of the learning organization - Peter M. Senge

Following the acclaim for Learning Outside the Classroom in 2012, this latest book more deeply explains how well constructed outdoor learning experiences can benefit children and young peopleÂ’s academic development and health and wellbeing.Outdoor Learning Across the Curriculum outlines the theory and practice to enable preservice and experienced primary and secondary school teachers to systematically incorporate meaningful outdoor learning opportunities into their daily teaching activities, in a range of environments and with diverse groups of students. Six of the chapters are substantially re-worked versions of the 2012 book, two are completely re-imagined, and four are entirely new. Topics for developing learning and teaching outdoors include:Inclusive educational designLearning for sustainabilityCommunity-based learningThe role of student curiosity and wonderEvidencing learningDeveloping a whole school approachPlace-responsive educationIntegrating digital technologyWith practical and engaging chapters containing aims, case studies, and guidelines for practice, this timely book provides teachers the tools with which they can integrate outdoor learning into their daily timetable. It will also be a valuable resource to other professions which use the outdoors for educational purposes.

Objev podobné jako Outdoor Learning Across the Curriculum - Heidi Smith, Simon Beames, Robbie Nicol, Peter Higgins

Level: EYFSSubject: EnglishAn engaging Writing activity bumper book to really help boost your childâ ™s progress at every stage of their learning! Fully in line with the Early Years Foundation Stage, this English book provides reassurance whilst supporting your childâ ™s learning at home.Combining useful English practice with engaging, colourful illustrations, this Writing bumper book helps to boost your childâ ™s confidence and develop good learning habits for life. Each fun activity is designed to give your child a real sense of achievement.Included in this book:questions that allow children to practise the important skills learned at schoolcolourful activities that make learning fun and motivate children to learn at homehelpful tips and answers so that you can support your childâ ™s learning

Objev podobné jako Writing Bumper Book Ages 3-5 - Collins Easy Learning