Machine learning Books

Book SynopsisFascinating' Greta ThunbergExtraordinary' Merlin SheldrakeA must-read' New ScientistEnthralling' George MonbiotBrilliant' Philip HoareWildlife filmmaker Tom Mustill had always liked whales. But when one breached onto his kayak, nearly killing him, he became obsessed.This book traces his extraordinary investigation into the deep ocean and the cutting-edge science of animal translation.What would it take to speak with a whale? Are we ready for what they might say?MORE PRAISE FOR HOW TO SPEAK WHALEOne of the most exciting and hopeful books I have read in ages' SY MONTGOMERY, AUTHOR OF THE SOUL OF AN OCTOPUSA narrative that will expand your concept of language and deepen your understanding of the many ways there are to be alive It left me inspired' MERLIN SHELDRAKE, AUTHOR OF ENTANGLED LIFEA must-read a hugely engaging personal story of a journey into the future of human-animal communication facilitated by delving into its past' NEW SCIENTISTFascinating and deeply humane' GRETA THUNBERGATrade Review‘A rich exploration of some of the world's most astonishing creatures … Mustill weaves a narrative that will expand your concept of language and deepen your understanding of the many ways there are to be alive. This is an extraordinary book that left me inspired’ Merlin Sheldrake, author of Entangled Life ‘A must-read… a hugely engaging personal story of a journey into the future of human-animal communication facilitated by delving into its past’ New Scientist ‘[An] extensively researched and energetic book… it is via the informed, far-reaching empathy of intermediaries such as Mustill that we stand our best chance of seeing into the non-human depths’ New Statesman ‘First-class … Reasoned, entertaining, and fact-filled’ Forbes ‘Fascinating and deeply humane’ Greta Thunberg ‘A rich, enthralling, brilliant book that opens our eyes and ears to worlds we can scarcely imagine’George Monbiot, Sunday Times bestselling author of Regenesis ‘Tantalizing … Think how transformative it would be if we could chat with whales about their love lives or their sorrows or their thoughts on the philosophy of language’ Elizabeth Kolbert, New Yorker ‘Mind-blowing … You will never feel closer to the magnificence of whales’Lucy Jones, author of Losing Eden ‘A scary, important and brilliant book … If we do get to translate ‘whale’, will we like what they’ve got to say?’Philip Hoare, author of Leviathan ‘Mustill takes us farther, much farther, than Dr. Dolittle ever imagined’ Carl Safina ‘Riveting … One of the most exciting and hopeful books I have read in ages’ Sy Montgomery, author of The Soul of an Octopus ‘Mustill conveys the richness of whale song and communication’ Frans de Waal ‘Lively and informative’ Jonathan Slaght, author of Owls of the Eastern Ice ‘Extraordinary’ Christiana Figueres

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Book SynopsisSometime in the future the intelligence of machines will exceed that of human brain power. So are we on the edge of an AI-pocalypse, with superintelligent devices superseding humanity, as predicted by Stephen Hawking? Or will this herald a kind of Utopia, with machines doing a far better job at complex tasks than us? You might not realise it, but you interact with AIs every day. They route your phone calls, approve your credit card transactions and help your doctor interpret results. Driverless cars will soon be on the roads with a decision-making computer in charge. But how do machines actually think and learn? In Machines That Think, AI experts and New Scientist explore how artificial intelligence helps us understand human intelligence, machines that compose music and write stories - and ask if AI is really a threat.ABOUT THE SERIESNew Scientist Instant Expert books are definitive and accessible entry points to the most important subjects in science; subjects that challenge, attract debate, invite controversy and engage the most enquiring minds. Designed for curious readers who want to know how things work and why, the Instant Expert series explores the topics that really matter and their impact on individuals, society, and the planet, translating the scientific complexities around us into language that's open to everyone, and putting new ideas and discoveries into perspective and context.

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Book Synopsis'Absorbing, mind-enlarging, studded with insights ... This could have significant real-world results' Sunday Times Humanity's greatest feat is our incredible ability to learn. Even in their first year, infants acquire language, visual and social knowledge at a rate that surpasses the best supercomputers. But how, exactly, do our brains learn? In How We Learn, leading neuroscientist Stanislas Dehaene delves into the psychological, neuronal, synaptic and molecular mechanisms of learning. Drawing on case studies of children who learned despite huge difficulty and trauma, he explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood. We can all enhance our learning and memory at any age and 'learn to learn' by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback and consolidation. The human braiTrade ReviewThis is an absorbing, mind-enlarging book, studded with insights ... Could have significant real-world results. -- James McConnachie * Sunday Times *An entertaining survey of how science from brain scans to psychological tests is helping inspire pedagogy. Dehaene challenges many tropes [and] describes much of his own pioneering work ... Well translated from the French with some touching references to his upbringing, from the cult film La Jetée to the writing of Daniel Pennac. -- Andrew Jack * Financial Times *An expert overview of learning ... Dehaene's fourth insightful exploration of neuroscience will pay dividends for attentive readers. * Kirkus *

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Book SynopsisThis seminal book injects the topic of superintelligence into the academic and popular mainstream. What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? In a tour de force of analytic thinking, Bostrom lays a foundation for understanding the future of humanity and intelligent life.Trade ReviewWorth reading. * Elon Musk, Founder of SpaceX and Tesla *I highly recommend this book * Bill Gates *very deep ... every paragraph has like six ideas embedded within it. * Nate Silver *Nick Bostrom makes a persuasive case that the future impact of AI is perhaps the most important issue the human race has ever faced. Instead of passively drifting, we need to steer a course. Superintelligence charts the submerged rocks of the future with unprecedented detail. It marks the beginning of a new era * Stuart Russell, Professor of Computer Science, University of California, Berkley *Those disposed to dismiss an 'AI takeover' as science fiction may think again after reading this original and well-argued book * Martin Rees, Past President, Royal Society *This superb analysis by one of the worlds clearest thinkers tackles one of humanitys greatest challenges: if future superhuman artificial intelligence becomes the biggest event in human history, then how can we ensure that it doesnt become the last? * Max Tegmark, Professor of Physics, MIT *Terribly important ... groundbreaking... extraordinary sagacity and clarity, enabling him to combine his wide-ranging knowledge over an impressively broad spectrum of disciplines - engineering, natural sciences, medicine, social sciences and philosophy - into a comprehensible whole... If this book gets the reception that it deserves, it may turn out the most important alarm bell since Rachel Carson's Silent Spring from 1962, or ever * Olle Haggstrom, Professor of Mathematical Statistics *Valuable. The implications of introducing a second intelligent species onto Earth are far-reaching enough to deserve hard thinking * The Economist *There is no doubting the force of [Bostrom's] arguments the problem is a research challenge worthy of the next generations best mathematical talent. Human civilisation is at stake * Financial Times *His book Superintelligence: Paths, Dangers, Strategies became an improbable bestseller in 2014 * Alex Massie, Times (Scotland) *Ein Text so nüchtern und cool, so angstfrei und dadurch umso erregender, dass danach das, was bisher vor allem Filme durchgespielt haben, auf einmal höchst plausibel erscheint. A text so sober and cool, so fearless and thus all the more exciting that what has until now mostly been acted through in films, all of a sudden appears most plausible afterwards. (translated from German) * Georg Diez, DER SPIEGEL *Worth reading.... We need to be super careful with AI. Potentially more dangerous than nukes * Elon Musk, Founder of SpaceX and Tesla *A damn hard read * Sunday Telegraph *I recommend Superintelligence by Nick Bostrom as an excellent book on this topic * Jolyon Brown, Linux Format *Every intelligent person should read it. * Nils Nilsson, Artificial Intelligence Pioneer, Stanford University *An intriguing mix of analytic philosophy, computer science and cutting-edge science fiction, Nick Bostrom's Superintelligence is required reading for anyone seeking to make sense of the recent surge of interest in artificial intelligence (AI). * Colin Garvey, Icon *Table of ContentsPreface 1: Past Developments and Present Capabilities 2: Roads to Superintelligence 3: Forms of Superintelligence 4: Singularity Dynamics 5: Decisive Strategic Advantage 6: Intellectual Superpowers 7: The Superintelligent Will 8: Is the Default Outcome Doom? 9: The Control Problem 10: Oracles, Genies, Sovereigns, Tools 11: Multipolar Scenarios 12: Acquiring Values 13: Design Choices 14: The Strategic Picture 15: Nut-Cutting Time Afterword

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Book SynopsisThis seminal book injects the topic of superintelligence into the academic and popular mainstream. What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? In a tour de force of analytic thinking, Bostrom lays a foundation for understanding the future of humanity and intelligent life.Trade ReviewWorth reading. * Elon Musk, Founder of SpaceX and Tesla *I highly recommend this book * Bill Gates *very deep ... every paragraph has like six ideas embedded within it. * Nate Silver *Nick Bostrom makes a persuasive case that the future impact of AI is perhaps the most important issue the human race has ever faced. Instead of passively drifting, we need to steer a course. Superintelligence charts the submerged rocks of the future with unprecedented detail. It marks the beginning of a new era * Stuart Russell, Professor of Computer Science, University of California, Berkley *Those disposed to dismiss an 'AI takeover' as science fiction may think again after reading this original and well-argued book * Martin Rees, Past President, Royal Society *This superb analysis by one of the worlds clearest thinkers tackles one of humanitys greatest challenges: if future superhuman artificial intelligence becomes the biggest event in human history, then how can we ensure that it doesnt become the last? * Max Tegmark, Professor of Physics, MIT *Terribly important ... groundbreaking... extraordinary sagacity and clarity, enabling him to combine his wide-ranging knowledge over an impressively broad spectrum of disciplines - engineering, natural sciences, medicine, social sciences and philosophy - into a comprehensible whole... If this book gets the reception that it deserves, it may turn out the most important alarm bell since Rachel Carson's Silent Spring from 1962, or ever * Olle Haggstrom, Professor of Mathematical Statistics *Valuable. The implications of introducing a second intelligent species onto Earth are far-reaching enough to deserve hard thinking * The Economist *There is no doubting the force of [Bostroms] arguments the problem is a research challenge worthy of the next generations best mathematical talent. Human civilisation is at stake * Financial Times *His book Superintelligence: Paths, Dangers, Strategies became an improbable bestseller in 2014 * Alex Massie, Times (Scotland) *Ein Text so nüchtern und cool, so angstfrei und dadurch umso erregender, dass danach das, was bisher vor allem Filme durchgespielt haben, auf einmal höchst plausibel erscheint. A text so sober and cool, so fearless and thus all the more exciting that what has until now mostly been acted through in films, all of a sudden appears most plausible afterwards. (translated from German) * Georg Diez, DER SPIEGEL *Worth reading.... We need to be super careful with AI. Potentially more dangerous than nukes * Elon Musk, Founder of SpaceX and Tesla *A damn hard read * Sunday Telegraph *I recommend Superintelligence by Nick Bostrom as an excellent book on this topic * Jolyon Brown, Linux Format *Every intelligent person should read it. * Nils Nilsson, Artificial Intelligence Pioneer, Stanford University *An intriguing mix of analytic philosophy, computer science and cutting-edge science fiction, Nick Bostrom's Superintelligence is required reading for anyone seeking to make sense of the recent surge of interest in artificial intelligence (AI). * Colin Garvey, Icon *Table of ContentsPreface ; 1. Past Developments and Present Capabilities ; 2. Roads to Superintelligence ; 3. Forms of Superintelligence ; 4. Singularity Dynamics ; 5. Decisive Strategic Advantage ; 6. Intellectual Superpowers ; 7. The Superintelligent Will ; 8. Is the Default Outcome Doom? ; 9. The Control Problem ; 10. Oracles, Genies, Sovereigns, Tools ; 11. Multipolar Scenarios ; 12. Acquiring Values ; 13. Design Choices ; 14. The Strategic Picture ; 15. Nut-Cutting Time

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Book SynopsisAn argument in favor of finding a place for humans (and humanness) in the future digital economy.In the digital economy, accountants, baristas, and cashiers can be automated out of employment; so can surgeons, airline pilots, and cab drivers. Machines will be able to do these jobs more efficiently, accurately, and inexpensively. But, Nicholas Agar warns in this provocative book, these developments could result in a radically disempowered humanity.The digital revolution has brought us new gadgets and new things to do with them. The digital revolution also brings the digital economy, with machines capable of doing humans' jobs. Agar explains that developments in artificial intelligence enable computers to take over not just routine tasks but also the kind of “mind work” that previously relied on human intellect, and that this threatens human agency. The solution, Agar argues, is a hybrid social-digital economy. The key value of the digital economy is efficien

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Book SynopsisA project-based guide to the basics of deep learning.This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.Each chapter includes a p

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Book SynopsisHow to develop robots that will be more like humans and less like computers, more social than machine-like, and more playful and less programmed.Most robots are not very friendly. They vacuum the rug, mow the lawn, dispose of bombs, even perform surgery—but they aren't good conversationalists. It's difficult to make eye contact. If the future promises more human-robot collaboration in both work and play, wouldn't it be better if the robots were less mechanical and more social? In How to Grow a Robot, Mark Lee explores how robots can be more human-like, friendly, and engaging.Developments in artificial intelligence—notably Deep Learning—are widely seen as the foundation on which our robot future will be built. These advances have already brought us self-driving cars and chess match-winning algorithms. But, Lee writes, we need robots that are perceptive, animated, and responsive—more like humans and less like computers, more social than mac

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Book SynopsisA clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart.Descriptions of AI's possible effects on businesses and their employees cycle between utopian hype and alarmist doomsaying. This book from MIT Sloan Management Review avoids both these extremes, providing instead a clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart. The contributors show that organizations can create business value with AI by cooperating with it rather than relinquishing control to it. The smartest companies know that they don't need AI that mimics humans because they already have access to resources with human capability—actual humans.The book acknowledges the prominent role of such leading technology companies as Facebook, Apple, Amazon, Netflix, and Google in applying AI to their busine

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Book SynopsisA hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides.Trade Review'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San DiegoTable of ContentsPart I. Understanding and Dealing with Big Data: 1. Introduction; 2. MapReduce; Part II. Big Data Frameworks: 3. Hadoop; 4. Spark; 5. Spark SQL and DataFrames; Part III. Machine Learning for Big Data: 6. Machine Learning with Spark; 7. Machine Learning for Big Data; 8. Implementing Classical Methods: k-means and Linear Regression; 9. Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment.

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Book SynopsisWith this practical book, subject matter experts will learn ways to develop strong, persuasive points when presenting data to different groups in their organizations.

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Book SynopsisWhat does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.Trade Review'In our data-rich world, probabilistic programming is what allows programmers to perform statistical inference in a principled way for use in automated decision making. This rapidly growing field, which has emerged at the intersection of machine learning, statistics and programming languages, has the potential to become the driving force behind AI. But probabilistic programs can be counterintuitive and difficult to understand. This edited volume gives a comprehensive overview of the foundations of probabilistic programming, clearly elucidating the basic principles of how to design and reason about probabilistic programs, while at the same time highlighting pertinent applications and existing languages. With its breadth of topic coverage, the book will serve as an important and timely reference for researchers and practitioners.' Marta Kwiatkowska, University of OxfordTable of Contents1. Semantics of Probabilistic Programming: A Gentle Introduction Fredrik Dahlqvist, Alexandra Silva and Dexter Kozen; 2. Probabilistic Programs as Measures Sam Staton; 3. An Application of Computable Distributions to the Semantics of Probabilistic Programs Daniel Huang, Greg Morrisett and Bas Spitters; 4. On Probabilistic λ-Calculi Ugo Dal Lago; 5. Probabilistic Couplings from Program Logics Gilles Barthe and Justin Hsu; 6. Expected Runtime Analysis by Program Verification Benjamin Lucien Kaminski, Joost-Pieter Katoen and Christoph Matheja; 7. Termination Analysis of Probabilistic Programs with Martingales Krishnendu Chatterjee, Hongfei Fu and Petr Novotný; 8. Quantitative Analysis of Programs with Probabilities and Concentration of Measure Inequalities Sriram Sankaranarayanan; 9. The Logical Essentials of Bayesian Reasoning Bart Jacobs and Fabio Zanasi; 10. Quantitative Equational Reasoning Giorgio Bacci, Radu Mardare, Prakash Panangaden and Gordon Plotkin; 11. Probabilistic Abstract Interpretation: Sound Inference and Application to Privacy José Manuel Calderón Trilla, Michael Hicks, Stephen Magill, Piotr Mardziel and Ian Sweet; 12. Quantitative Information Flow with Monads in Haskell Jeremy Gibbons, Annabelle McIver, Carroll Morgan and Tom Schrijvers; 13. Luck: A Probabilistic Language for Testing Lampropoulos Leonidas, Benjamin C. Pierce, Li-yao Xia, Diane Gallois-Wong, Cătălin Hriţcu and John Hughes; 14. Tabular: Probabilistic Inference from the Spreadsheet Andrew D. Gordon, Claudio Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel and Daniel Tarlow; 15. Programming Unreliable Hardware Michael Carbin and Sasa Misailovic.

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Book SynopsisThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. ''Data science'' and ''machine learning'' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.Table of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First-Century Topics: 15. Large-scale hypothesis testing and false-discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Author Index; Subject Index.

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Book SynopsisINTERNET OF HEALTHCARE THINGS The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making. The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emTable of ContentsPreface xiii Section 1: Security and Privacy Concern in IoHT 1 1 Data Security and Privacy Concern in the Healthcare System 3Ahuja Sourav 1.1 Introduction 3 1.2 Privacy and Security Concerns on E-Health Data 6 1.3 Levels of Threat to Information in Healthcare Organizations 6 1.4 Security and Privacy Requirement 9 1.5 Security of Healthcare Data 11 1.5.1 Existing Solutions 11 1.5.2 Future Challenges in Security and Privacy in the Healthcare Sector 15 1.5.3 Future Work to be Done in Security and Privacy in the Healthcare Sector 16 1.6 Privacy-Preserving Methods in Data 18 1.7 Conclusion 22 References 23 2 Authentication and Authorization Mechanisms for Internet of Healthcare Things 27Srinivasan Lakshmi Narasimhan 2.1 Introduction 28 2.2 Stakeholders in IoHT 29 2.3 IoHT Process Flow 31 2.4 Sources of Vulnerability 33 2.5 Security Features 34 2.6 Challenges to the Security Fabric 35 2.7 Security Techniques—User Authentication 36 2.8 Conclusions 37 References 38 3 Security and Privacy Issues Related to Big Data-Based Ubiquitous Healthcare Systems 41Jaspreet Singh 3.1 Introduction 41 3.2 Big Data Privacy & Security Issues 42 3.3 Big Data Security Problem 43 3.3.1 Big Data Security Lifecycle 44 3.3.2 Threats & Attacks on Big Data 47 3.3.3 Current Technologies in Use 48 3.4 Privacy of Big Data in Healthcare 50 3.4.1 Data Protection Acts 50 3.4.1.1 HIPAA Compliance 50 3.4.1.2 HIPAA Five Rules 53 3.5 Privacy Conserving Methods in Big Data 56 3.6 Conclusion 60 References 61 Section 2: Application of Machine Learning, Blockchain and Fog Computing on IoHT 65 4 Machine Learning Aspects for Trustworthy Internet of Healthcare Things 67Pradeep Bedi, S.B. Goyal, Jugnesh Kumar and Preetishree Patnaik 4.1 Introduction 68 4.2 Overview of Internet of Things 69 4.2.1 Application Area of IoT 72 4.2.1.1 Wearable Devices 73 4.2.1.2 Smart Home Applications 73 4.2.1.3 Healthcare IoT Applications 73 4.2.1.4 Smart Cities 73 4.2.1.5 Smart Agriculture 74 4.2.1.6 Industrial Internet of Things 74 4.3 Security Issues of IoT 74 4.3.1 Authentication 75 4.3.2 Integrity 75 4.3.3 Confidentiality 75 4.3.4 Non-Repudiation 75 4.3.5 Authorization 76 4.3.6 Availability 76 4.3.7 Forward Secrecy 76 4.3.8 Backward Secrecy 76 4.4 Internet of Healthcare Things (IoHT): Architecture and Challenges 76 4.4.1 IoHT Support 77 4.4.2 IoHT Architecture and Data Processing Stages 78 4.4.3 Benefits Associated With Healthcare Based on the IoT 80 4.4.4 Challenges Faced by IoHT 81 4.4.5 Needs in IoHT 81 4.5 Security Protocols in IoHT 82 4.5.1 Key Management 83 4.5.2 User/Device Authentication 83 4.5.3 Access Control/User Access Control 83 4.5.4 Intrusion Detection 83 4.6 Application of Machine Learning for Intrusion Detection in IoHT 84 4.7 Proposed Framework 86 4.8 Conclusion 90 References 90 5 Analyzing Recent Trends and Public Sentiment for Internet of Healthcare Things and Its Impact on Future Health Crisis 95Upendra Dwivedi 5.1 Introduction 96 5.2 Literature Review 97 5.3 Overview of the Internet of Healthcare Things 100 5.4 Performing Topic Modeling on IoHTs Dataset 104 5.5 Performing Sentiment Analysis on IoHTs Dataset 107 5.6 Conclusion and Future Scope 110 References 111 6 Rise of Telemedicine in Healthcare Systems Using Machine Learning: A Key Discussion 113Shaweta Sachdeva and Aleem Ali 6.1 Introduction 114 6.2 Types of Machine Learning 115 6.3 Telemedicine Advantages 115 6.4 Telemedicine Disadvantages 116 6.5 Review of Literature 116 6.6 Fundamental Key Components Needed to Begin Telemedicine 118 6.6.1 Collaboration Instruments 118 6.6.2 Clinical Peripherals 119 6.6.3 Work Process 119 6.6.4 Cloud-Based Administrations 119 6.7 Types of Telemedicine 119 6.7.1 Store-and-Forward Method 119 6.7.1.1 Telecardiology 120 6.7.1.2 Teleradiology 121 6.7.1.3 Telepsychiatry 121 6.7.1.4 Telepharmacy 121 6.7.2 Remote Monitoring 123 6.7.3 Interactive Services 123 6.8 Benefits of Telemedicine 124 6.9 Application of Telemedicine Using Machine Learning 125 6.10 Innovation Infrastructure of Telemedicine 125 6.11 Utilization of Mobile Wireless Devices in Telemedicine 126 6.12 Conclusion 127 References 128 7 Trusted Communication in the Healthcare Sector Using Blockchain 131Balasamy K. 7.1 Introduction 131 7.2 Overview of Blockchain 133 7.3 Medical IoT Concerns 134 7.3.1 Security Concerns 134 7.3.2 Privacy Concerns 135 7.3.3 Trust Concerns 135 7.4 Needs for Security in Medical IoT 135 7.5 Uses of Blockchain in Healthcare 137 7.6 Solutions for IoT Healthcare Cyber-Security 138 7.6.1 Architecture of the Smart Healthcare System 139 7.6.1.1 Data Perception Layer 139 7.6.1.2 Data Communication Layer 140 7.6.1.3 Data Storage Layer 140 7.6.1.4 Data Application Layer 140 7.7 Executions of Trusted Environment 140 7.7.1 Root of Trust Security Services 141 7.7.2 Chain of Trust Security Services 143 7.8 Patient Registration Using Medical IoT Devices 144 7.8.1 Encryption 145 7.8.2 Key Generation 146 7.8.3 Security by Isolation 146 7.8.4 Virtualization 146 7.9 Trusted Communications Using Blockchain 149 7.9.1 Record Creation Using IoT Gateways 150 7.9.2 Accessibility to Patient Medical History 151 7.9.3 Patient Enquiry With the Hospital Authority 151 7.9.4 Blockchain-Based IoT System Architecture 151 7.9.4.1 First Layer 151 7.9.4.2 Second Layer 152 7.9.4.3 Third Layer 152 7.10 Combined Workflows 152 7.10.1 Layer 1: The Gateway Collects IoT Data and Generates a New Record 152 7.10.2 Layer 2: Gateway/Authority Want to Access Patient’s Medical Record 153 7.10.3 Layer 3: Patient Visits and Interact With an Authority 153 7.11 Conclusions 154 References 154 8 Blockchain in Smart Healthcare Management 161Jayant Barak, Harshwardhan Chaudhary, Rakshit Mangal, Aarti Goel and Deepak Kumar Sharma 8.1 Introduction 162 8.2 Healthcare Industry 163 8.2.1 Classification of Healthcare Services 163 8.2.2 Health Information Technology (HIT) 164 8.2.3 Issues and Challenges Faced by Major Stakeholders in the Healthcare Industry 165 8.2.3.1 The Patient 166 8.2.3.2 The Pharmaceutical Industry 166 8.2.3.3 The Healthcare Service Providers 166 8.2.3.4 The Government 167 8.2.3.5 Insurance Company 167 8.3 Blockchain Technology 168 8.3.1 Important Terms 168 8.3.2 Features of Blockchain 170 8.3.2.1 Decentralization 170 8.3.2.2 Immutability 170 8.3.2.3 Transparency 171 8.3.2.4 Smart Contracts 171 8.3.3 Workings of a Blockchain System 171 8.3.4 Applications of Blockchain 173 8.3.4.1 Financial Services 173 8.3.4.2 Healthcare 173 8.3.4.3 Supply Chain 173 8.3.4.4 Identity Management 173 8.3.4.5 Voting 173 8.3.5 Challenges and Drawbacks of Blockchain 174 8.4 Applications of Blockchain in Healthcare 176 8.4.1 Electronic Medical Records (EMR) and Electronic Health Records (EHR) 176 8.4.2 Management System 177 8.4.3 Remote Monitoring/IoMT 178 8.4.4 Insurance Industry 179 8.4.5 Drug Counterfeiting 180 8.4.6 Clinical Trials 182 8.4.7 Public Health Management 182 8.5 Challenges of Blockchain in Healthcare 183 8.6 Future Research Directions 184 8.7 Conclusion 185 References 186 Section 3: Case Studies of Healthcare 189 9 Organ Trafficking on the Dark Web—The Data Security and Privacy Concern in Healthcare Systems 191Romil Rawat, Bhagwati Garg, Vinod Mahor, Shrikant Telang, Kiran Pachlasiya and Mukesh Chouhan 9.1 Introduction 192 9.2 Inclination for Cybersecurity Web Peril 194 9.3 Literature Review 197 9.4 Market Paucity or Organ Donors 199 9.5 Organ Harvesting and Transplant Tourism Revenue 203 9.6 Social Web Net Crimes 204 9.7 DW—Frontier of Illicit Human Harvesting 209 9.8 Organ Harvesting Apprehension 209 9.9 Result and Discussions 212 9.10 Conclusions 212 References 213 10 Deep Learning Techniques for Data Analysis Prediction in the Prevention of Heart Attacks 217C.V. Aravinda, Meng Lin, Udaya Kumar, Reddy K.R. and G. Amar Prabhu Abbreviations 218 10.1 Introduction 218 10.2 Literature Survey 219 10.3 Materials and Method 221 10.3.1 Cohort Study 222 10.4 Training Models 222 10.4.1 Artificial Neural Network (ANN) 222 10.4.2 K-Nearest Neighbor Classifier 224 10.4.3 Naïve Bayes Classifier 225 10.4.4 Decision Tree Classifier (DTC) 226 10.4.5 Random Forest Classifier (RFC) 226 10.4.6 Neural Network Implementation 226 10.5 Data Preparation 227 10.5.1 Multi-Layer Perceptron Neural Network (MLPNN) Algorithm and Prediction 227 10.6 Results Obtained 228 10.6.1 Accuracy 228 10.6.2 Data Analysis 228 10.7 Conclusion 236 References 236 11 Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT) 241Monika Lamba, Geetika Munjal and Yogita Gigras 11.1 Introduction 242 11.2 Related Work 245 11.3 IoT and Disease 250 11.4 Research Materials and Methods 251 11.4.1 Dataset 251 11.4.2 Data Pre-Processing 252 11.4.3 Classification Algorithms 252 11.5 Experimental Outcomes 253 11.6 Conclusion 257 References 258 12 Perspective-Based Studies of Trust in IoHT and Machine Learning-Brain Cancer 265Sweta Kumari, Akhilesh Kumar Sharma, Sandeep Chaurasia and Shamik Tiwari 12.1 Introduction 266 12.2 Literature Survey 267 12.3 Illustration of Brain Cancer 268 12.3.1 Brain Tumor 268 12.3.2 Types of Brain Tumors 269 12.3.3 Grades of Brain Tumors 270 12.3.4 Symptoms of Brain Tumors 271 12.4 Sleuthing and Classification of Brain Tumors 273 12.4.1 Sleuthing of Brain Tumors 273 12.4.2 Challenges During Classification of Brain Tumors 274 12.5 Survival Rate of Brain Tumors 274 12.6 Conclusion 278 References 279 Index 281

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Book SynopsisIn recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.Table of Contents1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen; 2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio; 3. Expressivity of deep neural networks Ingo Gühring, Mones Raslan and Gitta Kutyniok; 4. Optimization landscape of neural networks René Vidal, Zhihui Zhu and Benjamin D. Haeffele; 5. Explaining the decisions of convolutional and recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon and Klaus-Robert Müller; 6. Stochastic feedforward neural networks: universal approximation Thomas Merkh and Guido Montúfar; 7. Deep learning as sparsity enforcing algorithms A. Aberdam and J. Sulam; 8. The scattering transform Joan Bruna; 9. Deep generative models and inverse problems Alexandros G. Dimakis; 10. A dynamical systems and optimal control approach to deep learning Weinan E, Jiequn Han and Qianxiao Li; 11. Bridging many-body quantum physics and deep learning via tensor networks Yoav Levine, Or Sharir, Nadav Cohen and Amnon Shashua.

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Book SynopsisData assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.Table of Contents1. Prologue: linking 'The Future' with the present; 2. A data assimilation reminder; 3. Remembrance of things path; 4. SDA variational principles; Euler–Lagrange equations and Hamiltonian formulation; 5. Using waveform information; 6. Annealing in the model precision Rf; 7. Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations; 8. Monte Carlo methods; 9. Machine learning and its equivalence to statistical data assimilation; 10. Two examples of the practical use of data assimilation; 11. Unfinished business; Bibliography; Index.

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Book SynopsisWhat is an artificial intelligence (AI)-enabled drone and what can it do? Are AI-enabled drones better than human-controlled drones? This book will answer these questions and more, and empower you to develop your own AI-enabled drone.You''ll progress from a list of specifications and requirements, in small and iterative steps, which will then lead to the development of Unified Modeling Language (UML) diagrams based in part to the standards established by for the Robotic Operating System (ROS). The ROS architecture has been used to develop land-based drones. This will serve as a reference model for the software architecture of unmanned systems. Using this approach you''ll be able to develop a fully autonomous drone that incorporates object-oriented design and cognitive deep learning systems that adapts to multiple simulation environments. These multiple simulation environments will also allow you to further build public trustTable of ContentsIntelligent Autonomous Drones with Cognitive Deep LearningChapter 1. Defining the Required Goals, Specifications, and RequirementsChapter 2. UML Systems for Reliable and Robust AI enabled Self-Driving DronesChapter 3. Setting Your Main Virtual Linux SystemChapter 4. Understanding Advanced Anaconda ConceptsChapter 5. Understanding Drone-Kit for Testing and Programming your Self-Driving DroneChapter 6. Understanding, Maintaining, and Controlling the DRIVING Trajectory of the AI Rover DroneChapter 7. AI Enabled Rover Drone Vision with the Python OpenCV LibraryChapter 8. Your First Experience with Creating Drone Reinforcement Learning for Self-Driving and ExploringChapter 9. AI Enabled Rover Drones with Advanced Deep LearningChapter 10. Nature's other Secrets (Uncertainty, Bayesian Deep Learning, and Evolutionary Computing for Rovers)Chapter 11. Building the Ultimate Cognitive Deep Learning Land-Rover ControllerChapter 12. AI Drone Verification and Validation with Computer SimulationsChapter 13. The Critical Need for Geo-Spatial Guidance for AI Rover DronesChapter 14. Statistics and Experimental Algorithms for Drone EnhancementsChapter 15. The Robotic Operating System (ROS) Architecture for AI enabled Land-Based Rover Drones.Chapter 16. Putting it all together and the Testing Required.Chapter 17. “It’s Alive! It’s Alive!” (Facing Ones Very Own Creation)Chapter 18. Your Creation can be your Best Friend or your Worst Nightmare.

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Book SynopsisGo beyond the basics and build complete applications using the Rust programming language, updated for Rust 2021 edition. The applications you''ll build over the course of this book include a high-performance web client, an embedded computer (for a robot, for example), a game, a serverless web app, and an application that incorporates AI and machine learning.   Each chapter is organized in the following format: what the kind of should application look like; requirements and user stories of our example program; an introduction to the Rust libraries used; the actual implementation of the example program, including common pitfalls and their solutions; and a brief comparison of libraries for building each application, if there is no clear preference. Practical Rust Projects, Second Edition will open your eyes to how Rust can be put to practical, real-world use. After reading this book, you will be able to use Rust to build a varTable of Contents1. Welcome to the World of Rust2. Building a Command-Line Program3. Creating Graphical User Interfaces (GUIs)4. High-performance Web Frontend using WebAssembly5. Building REST APIs6. Going Serverless with Amazon AWS Rust SDK7. Building a Game8. Physical Computing in Rust9. Artificial Intelligence and Machine Learning10. What else can you do with Rust?---------------------------------------------------------1. Welcome to the World of Rust * Add a note on what's changed in the 2nd edition. * Add a note on Rust 20212. Building a Command-Line Program3. Creating Graphical User Interfaces (GUIs) * Upgrade to GTK 4?4. High-performance Web Frontend using WebAssembly5. Building REST APIs 6. 6. Going Serverless with Amazon AWS Rust SDK * Using the new AWS SDK for Rust and Rust runtime for AWS Lambda7. Building a Game * Use a different game engine because the one used in the 1st edition seized development8. Physical Computing in Rust * Maybe add a section on using WebAssembly on RPi9. Artificial Intelligence and Machine Learning * Maybe add a section on deep learning10. What else can you do with Rust? * Remove the web part that is included in 2nd edition

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Book SynopsisMigrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will LearnMaster the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learnWho This Book Is ForData scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.Table of ContentsChapter 1: An Easy Transition.- Chapter 2: Selecting Algorithms.- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark.- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark.-  Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark.- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark.- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark.- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark.- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark.- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark.- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark.- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark.- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark.- Chapter 17: Pipelines with Scikit-Learn and PySpark.- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.  

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Book SynopsisWhile many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems.

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Book SynopsisArtificial 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.

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Book SynopsisAt its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it’s important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

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Book SynopsisThe Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You’ll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features · Lots of examples based in the Spark Java APIs using real-life dataset and scenarios · Examples based on Spark v2.3 Ingestion through files, databases, and streaming · Building custom ingestion process · Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author BioAn experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he’s now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.

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Book SynopsisThe mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you'll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. about the technology It's important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Learning sets out the foundations of DL in a way that's both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you're done, you'll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge. Trade Review'This is a book that will reward your patience and perseverance with a clear and detailed knowledge of deep learning mathematics and associated techniques.' Tony Holdroyd 'Most online machine learning courses teach you how to get stuff done, but they don't give you the underlying math. If you want to know, this is the book for you!' Wiebe de Jong 'A really interesting book for people that want to understand the underlying mathematical mechanism of deep learning.' Julien Pohie 'Gives a unique perspective about machine learning and mathematical approaches.' Krzysztof Kamyczek 'An awesome book to get the grasp of the important mathematical skills to understand the very basics of deep learning.' Nicole KoenigsteinTable of Contentstable of contents READ IN LIVEBOOK 1AN OVERVIEW OF MACHINE LEARNING AND DEEP LEARNING READ IN LIVEBOOK 2INTRODUCTION TO VECTORS, MATRICES AND TENSORS FROM MACHINE LEARNING AND DATA SCIENCE POINT OF VIEW READ IN LIVEBOOK 3INTRODUCTION TO VECTOR CALCULUS FROM MACHINE LEARNING POINT OF VIEW READ IN LIVEBOOK 4LINEAR ALGEBRAIC TOOLS IN MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 5PROBABILITY DISTRIBUTIONS FOR MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 6BAYESIAN TOOLS FOR MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 7FUNCTION APPROXIMATION: HOW NEURAL NETWORKS MODEL THE WORLD READ IN LIVEBOOK 8TRAINING NEURAL NETWORKS: FORWARD AND BACKPROPAGATION READ IN LIVEBOOK 9LOSS, OPTIMIZATION AND REGULARIZATION READ IN LIVEBOOK 10ONE, TWO AND THREE DIMENSIONAL CONVOLUTION AND TRANSPOSED CONVOLUTION IN NEURAL NETWORKS 11 IMAGE ANALYSIS: 2D CONVOLUTION BASED NEURAL NETWORK ARCHITECTURES FOR OBJECT RECOGNITION AND DETECTION 12 VIDEO ANALYSIS: 3D CONVOLUTION BASED SPATIO TEMPORAL NEURAL NETWORK ARCHITECTURES READ IN LIVEBOOK APPENDIX A: APPENDIX A.1Dot Product and cosine of the angle between two vectors A.2Computing variance of Gaussian Distribution A.3Two Theorems in Statistic

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Book Synopsis"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.Trade Review"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 "Really easy to read and gives practical examples and easy to understand explanations of the concepts behind deep learning." Billy O'Callaghan "A tell-tale book that tells you all the secrets of deep learning!" Nikos Kanakaris "A great refresher of the old concepts explored in new and exciting ways. Manifold hypothesis steals the show!" Sayak Paul "One of the best books on this topic." Rauhsan Jha "The book is full of insights, useful both for the novice and the more experienced machine learning professional." Viton Vitanis "This is the book to read if you want to learn DL." Kjell Jansson "Francois explains everything in a very lucid & systematic manner, this approach of writing certainly gives confidence in users." Rauhsan Jha

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Book SynopsisOptimise the performance of your systems with practical experiments used by engineers in the world's most competitive industries. Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You will start with a deep dive into methods like A/B testing and then graduate to advanced techniques used to measure performance in industries such as finance and social media. You will learn how to: Design, run, and analyse an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimisation Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation By the time you're done, you will be able to seamlessly deploy experiments in production, whilst avoiding common pitfalls. About the technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world's most competitive industries and will help you enhance machine learning systems, software applications, and quantitative trading solutions.Trade Review"Putting an 'improved' version of a system into production can be really risky. This book focuses you on what is important!" Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland "A must-have for anyone setting up experiments, from A/B tests to contextual bandits and Bayesian optimization." Maxim Volgin, KLM "Shows a non-mathematical programmer exactly what they need to write powerful mathematically-based testing algorithms." Patrick Goetz, The University of Texas at Austin "Gives you the tools you need to get the most out of your experiments." Marc-Anthony Taylor, Raiffeisen Bank International

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Book SynopsisMachine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code! You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You will even discover when not to use machine learning—and the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike. Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt. Trade Review“Anice view on practical data science and machine learning. Great reading fornewbies, some interesting views for seasoned practitioners.” Johannes Verwijnen “Amust read for those looking to balance the planning and experimentationlifecycle.” Jesús Antonino Juárez Guerrero “Apractical book to help engineers understand the workflow of machine learningprojects.” Xiangbo Mao “Donot implement your ML model into production without reading this book!” Lokesh Kumar

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Book SynopsisKubernetes is an essential tool for anyone deploying and managing cloud-native applications. It lays out a complete introduction to container technologies and containerized applications along with practical tips for efficient deployment and operation. This revised edition of the bestselling Kubernetes in Action contains new coverage of the Kubernetes architecture, including the Kubernetes API, and a deep dive into managing a Kubernetes cluster in production. In Kubernetes in Action, Second Edition, you'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling Kubernetes in Action, Second Edition teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. In this revised and expanded second edition, you'll take a deep dive into the structure of a Kubernetes-based application and discover how to manage a Kubernetes cluster in production. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling.Table of Contentstable of contents detailed TOC PART 1: FIRST TIME ON A BOAT: INTRODUCTION TO KUBERNETES READ IN LIVEBOOK 1INTRODUCING KUBERNETES READ IN LIVEBOOK 2UNDERSTANDING CONTAINERS READ IN LIVEBOOK 3DEPLOYING YOUR FIRST APPLICATION PART 2: LEARNING THE ROPES: KUBERNETES API OBJECTS READ IN LIVEBOOK 4INTRODUCING KUBERNETES API OBJECTS READ IN LIVEBOOK 5RUNNING WORKLOADS IN PODS READ IN LIVEBOOK 6MANGING THE POD LIFECYCLE READ IN LIVEBOOK 7ATTACHING STORAGE VOLUMES TO PODS READ IN LIVEBOOK 8PERSISTING DATA IN PERSISTENTVOLUMES READ IN LIVEBOOK 9CONFIGURATION VIA CONFIGMAPS, SECRETS, AND THE DOWNWARD API READ IN LIVEBOOK 10ORGANIZING OBJECTS USING NAMESPACES AND LABELS READ IN LIVEBOOK 11EXPOSING PODS WITH SERVICES READ IN LIVEBOOK 12EXPOSING SERVICES WITH INGRESS READ IN LIVEBOOK 13REPLICATING PODS WITH REPLICASETS READ IN LIVEBOOK 14MANAGING PODS WITH DEPLOYMENTS 15 DEPLOYING STATEFUL WORKLOADS WITH STATEFULSETS 16 DEPLOYING SPECIALIZED WORKLOADS WITH DAEMONSETS, JOBS, AND CRONJOBS PART 3: GOING BELOW DECK: KUBERNETES INTERNALS 17 UNDERSTANDING THE KUBERNETES API IN DETAIL 18 UNDERSTANDING THE CONTROL PLANE COMPONENTS 19 UNDERSTANDING THE CLUSTER NODE COMPONENTS 20 UNDERSTANDING THE INTERNAL OPERATION OF KUBERNETES CONTROLLERS PART 4: SAILING OUT TO HIGH SEAS: MANAGING KUBERNETES 21 DEPLOYING HIGHLY-AVAILABLE CLUSTERS 22 MANAGING THE COMPUTING RESOURCES AVAILABLE TO PODS 23 ADVANCED SCHEDULING USING AFFINITY AND ANTI-AFFINITY 24 AUTOMATIC SCALING USING THE HORIZONTALPODAUTOSCALER 25 SECURING THE API USING ROLE-BASED ACCESS CONTROL 26 PROTECTING CLUSTER NODES 27 SECURING NETWORK COMMUNICATION USING NETWORKPOLICIES 28 UPGRADING, BACKING UP, AND RESTORING KUBERNETES CLUSTERS 29 ADDING CENTRALIZED LOGGING, METRICS, ALERTING, AND TRACING PART 5: BECOMING A SEASONED MARINER: MAKING THE MOST OF KUBERNETES 30 KUBERNETES DEVELOPMENT AND DEPLOYMENT BEST PRACTICES 30 EXTENDING KUBERNETES WITH CUSTOMRESOURCEDEFINITIONS AND OPERATORS

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Book SynopsisPractical patterns for scaling machine learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Construct machine learning pipelines with data ingestion, distributed training, model serving, and more Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade offs between different patterns and approaches Manage and monitor machine learning workloads at scale Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns, you'll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you'll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. about the technology Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure. about the book Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you've mastered these cutting edge techniques, you'll put them all into practice and finish up by building a comprehensive distributed machine learning system.Trade Review'This is a really well thought out book on the problem of dealing with machine learning in a distributed environment.' Richard Vaughan 'A sound introduction to the exciting field of distributed ml for practitioners.' Pablo Roccat 'I came away with a greater familiarity with distributed training ideas, problems, and solutions.' Matt SarmientoTable of Contentstable of contents PART 1: BASIC CONCEPTS AND BACKGROUND READ IN LIVEBOOK 1INTRODUCTION TO DISTRIBUTED MACHINE LEARNING SYSTEMS PART 2: PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS READ IN LIVEBOOK 2DATA INGESTION PATTERNS READ IN LIVEBOOK 3DISTRIBUTED TRAINING PATTERNS READ IN LIVEBOOK 4MODEL SERVING PATTERNS READ IN LIVEBOOK 5WORKFLOW PATTERNS READ IN LIVEBOOK 6OPERATION PATTERNS PART 3: BUILDING A DISTRIBUTED MACHINE LEARNING PIPELINE 7 OVERVIEW OF PROJECT ARCHITECTURE 8 OVERVIEW OF RELEVANT TECHNOLOGIES 9 A COMPLETE IMPLEMENTATION

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Book SynopsisThe go-to guide in machine learning projects from design to production. No ML skills required! In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including: Understanding an ML project's requirements Setting up the infrastructure for the project and resourcing a team Working with clients and other stakeholders Dealing with data resources and bringing them into the project for use Handling the lifecycle of models in the project Managing the application of ML algorithms Evaluating the performance of algorithms and models Making decisions about which models to adopt for delivery Taking models through development and testing Integrating models with production systems to create effective applications Steps and behaviours for managing the ethical implications of ML technology About the technology Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects. You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear — this book lays out the unique practices you will need to ensure your projects succeed!Trade Review"There's a lot of knowledge in this book that most machine learning practitioners usually only discover after several failures & attempts in trying to deliver their ML projects." Richard Dze "Gives great insights to the problems and solutions of not only ML Projects but also data analysis and data science projects." Marvin Schwarze "The manual on managing ML projects for less experienced managers." Maxim Volgin

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Book SynopsisFascinating' Greta ThunbergExtraordinary' Merlin SheldrakeA must-read' New ScientistEnthralling' George MonbiotBrilliant' Philip HoareAs a biologist and nature filmmaker, Tom Mustill had always liked whales. But when one landed on his kayak, nearly killing him, the video clip of the event going viral, he became obsessed.This book traces an extraordinary investigation into the deep ocean and today's cutting-edge science. Using underwater ears,' robotic fish, big data and machine intelligence, leading scientists and tech-entrepreneurs across the world are working to turn the fantasy of Dr Dolittle into a reality, upending much of what we know about these mysterious creatures. But what would it mean if we were to make contact? Can we hope to one day understand animals? Are we ready for what they might say?Enormously original and hugely entertaining, How to Speak Whale is an unforgettable look at how close we truly are to communicating with another species and how doing so might change our world beyond recognition.Trade Review‘A rich exploration of some of the world's most astonishing creatures … Mustill weaves a narrative that will expand your concept of language and deepen your understanding of the many ways there are to be alive. This is an extraordinary book that left me inspired’ Merlin Sheldrake, author of Entangled Life ‘A must-read… a hugely engaging personal story of a journey into the future of human-animal communication facilitated by delving into its past’ New Scientist ‘[An] extensively researched and energetic book… it is via the informed, far-reaching empathy of intermediaries such as Mustill that we stand our best chance of seeing into the non-human depths’ New Statesman ‘First-class … Reasoned, entertaining, and fact-filled’ Forbes ‘Fascinating and deeply humane’ Greta Thunberg ‘A rich, enthralling, brilliant book that opens our eyes and ears to worlds we can scarcely imagine’George Monbiot, Sunday Times bestselling author of Regenesis ‘Tantalizing … Think how transformative it would be if we could chat with whales about their love lives or their sorrows or their thoughts on the philosophy of language’ Elizabeth Kolbert, New Yorker ‘Mind-blowing … You will never feel closer to the magnificence of whales’Lucy Jones, author of Losing Eden ‘A scary, important and brilliant book … If we do get to translate ‘whale’, will we like what they’ve got to say?’Philip Hoare, author of Leviathan ‘Mustill takes us farther, much farther, than Dr. Dolittle ever imagined’ Carl Safina ‘Riveting … One of the most exciting and hopeful books I have read in ages’ Sy Montgomery, author of The Soul of an Octopus ‘Mustill conveys the richness of whale song and communication’ Frans de Waal ‘Lively and informative’ Jonathan Slaght, author of Owls of the Eastern Ice ‘Extraordinary’ Christiana Figueres

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Book SynopsisBridges the gap between AI and neuroscience by telling the story of how the brain came to be.''I found this book amazing'' Daniel Kahneman, Winner of the Nobel Prize in Economics and bestselling author of Thinking Fast & SlowThe entirety of the human brain's 4-billion-year story can be summarised as the culmination of five evolutionary breakthroughs, starting from the very first brains, all the way to the modern human brains. Each breakthrough emerged from new sets of brain modifications, and equipped animals with a new suite of intellectual faculties.These five breakthroughs are the organising map to this book, and they make up our itinerary for our adventure back in time. Each breakthrough also has fascinating corollaries to breakthroughs in AI. Indeed, there will be plenty of such surprises along the way. For instance: the innovation that enabled AI to beat humans in the game of Go temporal difference reinforcement learning was an innovation discovered by our fish ancestors over 500 million years ago. The solutions to many of the current mysteries in AI such as common sense' can be found in the tiny brain of a mouse. Where do emotions come from? Research suggests that they may have arisen simply as a solution to navigation in ancient worm brains. Unravelling this evolutionary story will reveal the hidden features of human intelligence and with them, just how your mind came to be.Trade Review‘Max Bennett published two scientific papers on brain evolution that blew me away. Now he has turned these into a fabulous book, A Brief History of Intelligence. His friendly writing style, clear jargon-free prose, and well of information make this book a winner.’ Joseph LeDoux, Henry and Lucy Moses Professor of Neural Science & Psychology at NYU, bestselling author of Anxious and A Deep History of Ourselves ‘With a truly mindboggling scope, A Brief History of Intelligence integrates the most relevant scientific knowledge to paint the big picture of how the human mind emerged. The red line through this book never gets blurred by unnecessary detail or jargoned language. It makes for exciting reading for virtually everybody, laypersons and experts alike.’ Kurt Kotrschal, Professor at Department of Behavioral Biology at University of Vienna, author of Dog & Human: The secret of our soul mates ‘If you’re in the least bit curious about that 3-pound grey blob between your ears, read this book. Max Bennett’s entertaining and enlightening natural history of brains is a tour de force–as refreshing as it is entertaining. It made my brain happy.’ Jonathan Balcombe, bestselling author of Super Fly, and the New York Times bestseller What a Fish Knows ‘Max Bennett gives a lively account of how brains evolved, and how the brain works today. A Brief History of Intelligence is engaging, comprehensive, and brimming with novel insights.’ Kent Berridge, James Olds Distinguished Professor of Psychology & Neuroscience at University of Michigan ‘If you want to know how our ancestors were able to “weaponize their imaginarium to survive”, then this is the book for you. In fact, this book discloses everything you always wanted to know about the brain (but were afraid to ask). It is an incredible resource.’ Karl Friston, Scientific Director for Wellcome Centre for Human Neuroimaging; Professor at Queen Square Institute of Neurology, University College London

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Book Synopsis

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Book SynopsisA guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right.Making a case against technochauvinism—the belief that technology is always the solution̵

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Book SynopsisKey concepts, definitions, examples, and historical contexts for understanding smart cities, along with discussions of both drawbacks and benefits of this approach to urban problems.Over the past ten years, urban planners, technology companies, and governments have promoted smart cities with a somewhat utopian vision of urban life made knowable and manageable through data collection and analysis. Emerging smart cities have become both crucibles and showrooms for the practical application of the Internet of Things, cloud computing, and the integration of big data into everyday life. Are smart cities optimized, sustainable, digitally networked solutions to urban problems? Or are they neoliberal, corporate-controlled, undemocratic non-places? This volume in the MIT Press Essential Knowledge series offers a concise introduction to smart cities, presenting key concepts, definitions, examples, and historical contexts, along with discussions of both the drawbacks and the benefits of

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Book SynopsisTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.

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Book SynopsisIn 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.

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Book SynopsisThe design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

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Book SynopsisThis practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications

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Book SynopsisThis practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

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Book SynopsisThis practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work.

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Book SynopsisIn this book, author Matheus Facure explains the untapped potential of causal inference for estimating impacts and effects.

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