The If Machine, 2nd edition PDF Download
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Author: Peter Worley
Publisher: Bloomsbury Publishing
ISBN: 1472969065
Category : Education
Languages : en
Pages : 232
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Book Description
Fully updated edition with five brand new sessions and bonus features. Fully updated with new developments in the theory and practice of teaching philosophy, this new edition of the bestselling book, The If Machine, presents 30 clear, ready-to-use plans to teach philosophy in the classroom with children aged five to 13. Each tried-and-tested session offers an imaginary situation, followed by a series of questions to encourage children to challenge key philosophical ideas such as values and ethics, gender and identity, and existence and beauty. With a star system indicating the level of difficulty, this practical book by Peter Worley, founder of The Philosophy Foundation, outlines Peter's philosophical enquiry method, which he has developed over 20 years of teaching. This fantastic resource also includes a new hints and tips section, as well as a troubleshooting table with guidance and links to further resources about how to deal with common problems such as unanimity. Accompanied by a companion website featuring an introduction to the philosophy that inspired the ideas, The If Machine is a must-have resource for all classrooms.
Author: Peter Worley
Publisher: Bloomsbury Publishing
ISBN: 1472969065
Category : Education
Languages : en
Pages : 232
Get Book
Book Description
Fully updated edition with five brand new sessions and bonus features. Fully updated with new developments in the theory and practice of teaching philosophy, this new edition of the bestselling book, The If Machine, presents 30 clear, ready-to-use plans to teach philosophy in the classroom with children aged five to 13. Each tried-and-tested session offers an imaginary situation, followed by a series of questions to encourage children to challenge key philosophical ideas such as values and ethics, gender and identity, and existence and beauty. With a star system indicating the level of difficulty, this practical book by Peter Worley, founder of The Philosophy Foundation, outlines Peter's philosophical enquiry method, which he has developed over 20 years of teaching. This fantastic resource also includes a new hints and tips section, as well as a troubleshooting table with guidance and links to further resources about how to deal with common problems such as unanimity. Accompanied by a companion website featuring an introduction to the philosophy that inspired the ideas, The If Machine is a must-have resource for all classrooms.
Author: Peter Worley
Publisher: Bloomsbury Publishing
ISBN: 1441136827
Category : Education
Languages : en
Pages : 216
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Book Description
Each session in this practical book offers an imaginary situation, followed by a series of questions to encourage children to challenge key philosophical ideas such as values and ethics, gender and identity, and existence and beauty. All the enquiries have been tried and tested, and a handy star system is included to indicate the difficulty level of each one. With a comprehensive introduction and key sections on the philosophy behind the experiments, this book also includes an online teacher's resource to guide practitioners through using the sessions to best effect in the classroom.
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
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Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Author: Stephen Marsland
Publisher: CRC Press
ISBN: 1420067192
Category : Business & Economics
Languages : en
Pages : 407
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Book Description
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but
Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262028182
Category : Computers
Languages : en
Pages : 639
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Book Description
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
Author: Ian H. Witten
Publisher: Elsevier
ISBN: 0080890369
Category : Computers
Languages : en
Pages : 665
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Book Description
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Author: Peter Worley
Publisher: A&C Black
ISBN: 1441174958
Category : Education
Languages : en
Pages : 204
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Book Description
Using Homer's epic tales as inspiration, this book offers teachers session plans and storytelling tips to facilitate philosophical discussions with children aged 9-14.
Author: Denis Rothman
Publisher: Packt Publishing Ltd
ISBN: 1839212810
Category : Computers
Languages : en
Pages : 579
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Book Description
Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google TranslateUnderstand chained algorithms combining unsupervised learning with decision treesSolve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graphLearn about meta learning models with hybrid neural networksCreate a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data loggingBuilding conversational user interfaces (CUI) for chatbotsWriting genetic algorithms that optimize deep learning neural networksBuild quantum computing circuitsWho this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.
Author: Andrew W. Trask
Publisher: Simon and Schuster
ISBN: 163835720X
Category : Computers
Languages : en
Pages : 475
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Book Description
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
Author: Peter Worley
Publisher: Crown House Publishing
ISBN: 1781350612
Category : Education
Languages : en
Pages : 374
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Book Description
Imagine a one-stop shop stacked to the rafters with everything you could ever want to tap into young people's natural curiosity and get them thinking deeply. Well, this is it! Edited by professional philosopher Peter Worley from The Philosophy Shop and with a foreword by Ian Gilbert, this book is jam-packed with ideas, stimuli, thought experiments, activities, short stories, pictures and questions to get young people thinking philosophically. Primarily aimed at teachers to use as a stimuli for philosophical enquiries in the classroom or even as starter activities to get them thinking from the off, it can also be used by parents for some great family thinking or indeed anyone fed up of being told what to think (or urged not to think) and who wants a real neurological workout. The proceeds of the book are going towards The Philosophy Foundation charity.
