Machine learning Books

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 SynopsisThis comprehensive text on Python programming is designed for undergraduate and postgraduate students in Computer Science and Information Technology. Whether you are a beginner or have limited programming knowledge.

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Book SynopsisThe adoption of data analytics has remained remarkably static - perhaps reaching no more than thirty percent of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations.

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

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Book SynopsisThis practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.

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Book SynopsisThe past decade has witnessed extraordinary advances in artificial intelligence. But what precisely is it and where does its future lie?In this brilliant, one-stop guide WIRED journalist Matt Burgess explains everything you need to know about AI. He describes how it works. He looks at the ways in which it has already brought us everything from voice recognition software to self-driving cars, and explores its potential for further revolutionary change in almost every area of our daily lives. He examines the darker side of machine learning: its susceptibility to hacking; its tendency to discriminate against particular groups; and its potential misuse by governments. And he addresses the fundamental question: can machines become as intelligent as human beings?Trade ReviewIn this book Burgess manages to cover all the key AI trends and developments over the last 60 years . . . delivers an informative and readable guide to all the main events that have taken place to date . . . We found this one helpful for a new or general reader and would recommend it to those looking for a good place to start in this field. * Irish Tech News *

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Book SynopsisBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process DESCRIPTION Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. about the technology Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts. about the book Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.Table of Contentstable of contents detailed TOC PART 1: TIME WAITS FOR NO ONE READ IN LIVEBOOK 1UNDERSTANDING TIME SERIES FORECASTING READ IN LIVEBOOK 2A NAÏVE PREDICTION OF THE FUTURE READ IN LIVEBOOK 3GOING ON A RANDOM WALK PART 2: FORECASTING WITH STATISTICAL MODELS READ IN LIVEBOOK 4MODELING A MOVING AVERAGE PROCESS READ IN LIVEBOOK 5MODELING AN AUTOREGRESSIVE PROCESS READ IN LIVEBOOK 6MODELING COMPLEX TIME SERIES READ IN LIVEBOOK 7FORECASTING NON-STATIONARY TIME SERIES READ IN LIVEBOOK 8ACCOUNTING FOR SEASONALITY READ IN LIVEBOOK 9ADDING EXTERNAL VARIABLES TO OUR MODEL READ IN LIVEBOOK 10FORECASTING MULTIPLE TIME SERIES READ IN LIVEBOOK 11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIA PART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNING READ IN LIVEBOOK 12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTING READ IN LIVEBOOK 13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNING READ IN LIVEBOOK 14BABY STEPS WITH DEEP LEARNING READ IN LIVEBOOK 15REMEMBERING THE PAST WITH LSTM READ IN LIVEBOOK 16FILTERING OUR TIME SERIES WITH CNN READ IN LIVEBOOK 17USING PREDICTIONS TO MAKE MORE PREDICTIONS READ IN LIVEBOOK 18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLD PART 4: AUTOMATING FORECASTING AT SCALE READ IN LIVEBOOK 19AUTOMATING TIME SERIES FORECASTING WITH PROPHET READ IN LIVEBOOK 20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA 21 GOING ABOVE AND BEYOND APPENDIX APPENDIX A: INSTALLATION INSTRUCTIONS

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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 SynopsisThis book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.

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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 SynopsisUsing hands-on examples, this practical book shows data scientists and data practitioners how to build their own custom knowledge graphs. Authors Jesus Barrasa and Jim Webber from Neo4j illustrate patterns commonly used for building knowledge graphs that solve many of today's pressing problems.

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

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

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Book SynopsisMachine Learning is an integral tool in a business analyst's arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing Table of Contents1. Introduction to Machine Learning for Data Analytics 2. Role of Machine Learning in Promoting Sustainability 3. Addressing the Utilization of Popular Regression Models in business applications 4. CHATBOTS: The Uses and Impact In The Hospitality Sector 5. Traversing Through the Use of Robotics in Medical Industry: Outlining Emerging Trends and Perspectives for Future Growth 6. Integration of AI in Insurance and Health Care: What Does It Mean? 7. Artificial Intelligence in Agriculture – A Review 8. Machine Learning and Artificial Intelligence-based Tools in Digital Marketing: An Integrated Approach 9. Application Of Artificial Intelligence In Market Knowledge And B2b Marketing Co-Creation 10 A Systematic Literature Review of Artificial Intelligence's Impact on Customer Experience 11. The Impact of Artificial Intelligence on Customer Experience and the Purchasing Process 12. Application of Artificial Intelligence in Banking – A Review 13. Digital Ethics: Towards a socially preferable development of AI systems

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Book SynopsisMachine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.Trade Review"If you are considering implementing machine learning in your business but don’t know where to start, this is the right book for you. Machine Learning for Managers is a comprehensive but non-technical introduction to the topic with many relevant examples and implementation guidelines. The split into a detailed overview and project management instructions is ideal for readers who don’t have the time to acquire programming skills but are passionate about leveraging AI to enhance business performance. The author’s very engaging writing style makes reading a book about a potentially very dry topic enjoyable."Christoph Schumacher, Professor of Innovation and Economics; Director Knowledge Exchange Hub, Massey University, New Zealand"This book fills an important gap between pure-technical and pure-managerial descriptions of machine learning (ML). Written in a no-nonsense light-hearted style, it is easy to follow, yet doesn’t shy away from using technical terms that are important for managers to be able to speak to their ML engineers. Highly recommended for managers looking to understand more about what is under the hood of ML."Tava Olsen, Professor, Deputy Dean, Melbourne Business School"Machine Learning for Managers is a safe haven for non-technical readers interested in understanding what AI and specifically ML is about. With clear, direct and witty language, Geertsema ensures that our journey into AI is like a walk in the park. It is easy, pleasurable and refreshing in its approach and powerful in its choice of illustrations. It brings to the forefront key concepts such as explainability, governance and business case making the message lucent and highly applicable to managers interested in incorporating ML into their business. As a practitioner focussed on human centric AI, I am particularly keen in bringing down AI/ML from its ivory tower status. This book is exactly a tool for this as it provides transparency, deciphers otherwise perceived complex language and is the basis for what ML should do best: to serve you. By far the best introductory ML roadmap I have come across. A must read."Jose Romano, Senior Manager at the European Investment Fund and former Entrepreneur in Residence at TAZI.AI"The two complementary parts of the book form a comprehensive and practical guide to machine learning. The first part provides a nontechnical overview of machine learning algorithms, demystifying the jargon in the field, which is crucial for students, lecturers and practitioners aiming to apply machine learning to resolve real-life business problems. The second part insightfully examines how machine learning outcomes can be developed and deployed in the organisation's processes. A recommended work for anyone looking to successfully manage the tsunami of big data!"Leo Paas, Professor, The University of Auckland Business School; Program Director, Master of Business Analytics"This book provides an outstanding introduction to machine learning from a management perspective. It gives a very clear presentation of the state-of-the-art machine learning methods and how to manage machine learning projects efficiently. It brings a fresh, unique focus on how to learn machine learning from a business perspective. It is highly practical and discusses in detail how a machine learning project should be deployed in real business applications. Not to be missed by any manager with a serious interest in AI and Machine Learning."Albert Bifet, Professor, Director of the AI Institute, The University of Waikato, New ZealandTable of ContentsPart 1: Understanding Machine Learning 1. Let's jump right in 2. Different kinds of ML 3. Creating ML models 4. Linear models 5. Neural networks 6. Tree-based approaches, ensembles and boosting 7. Dimensionality reduction and clustering 8. Unstructured data 9. Explainable AI Part 2: Managing Machine Learning Projects 10. The ML system lifecycle 11. The big picture 12. Creating value with ML 13. Making the business case 14. The ML pipeline 15. Development 16. Deployment and monitoring

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Book SynopsisYour training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts

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Book SynopsisMachine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You'll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbours method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You'll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you'll need in practice. Additional features: How to avoid common problems, suTrade Review"In contrast to other books about machine learning, there is a bigger emphasis on programming and usage in practice. In particular, there is an excellent explanation of how to avoid over/under-fitting, and how to use cross-validation. This book is sure to be helpful for students who are interested to understand the core concepts, as well as their practical implementations in R."—Toby Dylan Hocking, Assistant Professor, Northern Arizona University"The Art of Machine Learning by Norman Matloff is a welcome addition to a growing body of books about machine learning. Matloff, whose career spans both computer science and statistics, addresses the new and exciting field with a fresh approach."—Dirk Eddelbuettel, Department of Statistics, University of IllinoisTable of ContentsAcknowledgmentsIntroductionPART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODSChapter 1: Regression ModelsChapter 2: Classification ModelsChapter 3: Bias, Variance, Overfitting, and Cross-ValidationChapter 4: Dealing with Large Numbers of FeaturesPART II: TREE-BASED METHODSChapter 5: A Step Beyond k-NN: Decision TreesChapter 6: Tweaking the TreesChapter 7: Finding a Good Set of HyperparametersPART III: METHODS BASED ON LINEAR RELATIONSHIPSChapter 8: Parametric MethodsChapter 9: Cutting Things Down to Size: RegularizationPART IV: METHODS BASED ON SEPARATING LINES AND PLANESChapter 10: A Boundary Approach: Support Vector MachinesChapter 11: Linear Models on Steroids: Neural NetworksPART V: APPLICATIONSChapter 12: Image Classification Chapter 13: Handling Time Series and Text Data Appendix A: List of Acronyms and Symbols Appendix B: Statistics and ML Terminology CorrespondenceAppendix C: Matrices, Data Frames, and Factor ConversionsAppendix D: Pitfall: Beware of “p-Hacking”!

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Book SynopsisThis book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. Trade Review“This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view.” (T. C. Mohan, zbMATH 1441.68001, 2020)Table of ContentsIntroductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.

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Book SynopsisThis book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics. Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.Table of ContentsChapter1. Studying the Weaponization of Social Media: Case Studies of Anti-NATO Disinformation Campaigns.- Chapter2. Cognitively-Inspired Inference for Malware Task Indentation.- Chapter3. Beyond the ‘Silk Road’: Assessing Illicit Drug Marketplaces on the Public Web.- Chapter4. Protecting the Web from Misinformation.- Chapter5. Social Media for Mental Health: Data, Methods, and Findings.- Chapter6. Twitter Bots and the Swedish Election.- Chapter7. Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study.- Chapter8. You are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter.- Chapter9. Inferring Systemic Nets with Applications to Islamist Forums.

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Book SynopsisCovid-19 has shown us the importance of mathematical and statistical models to interpret reality, provide forecasts, and explore future scenarios. Algorithms, artificial neural networks, and machine learning help us discover the opportunities and pitfalls of a world governed by mathematics and artificial intelligence.Trade Review“Alfio Quarteroni invites us to the stage of contemporary science and technology in which multidisciplinarity and transferability are combined to contribute to the construction of the wisdom of life … . A great master. Its access does not present difficulties beyond the decision to satisfy an intellectual and spiritual curiosity with a future edge: a book to be read with ease and understood with great quality.” (Melio Sáenz, ResearchGate, researchgate.net, June, 2023)Table of Contents1 Epidemic.- 2 Retrospective.- 3 Interlude: the revolution that did not happen and the revolution that was unforeseen.- 4 Artificial intelligence, learning computers, artificial neural networks.- 5 A bit of maths (behind artificial intelligence and machine learning).- 6 BIG DATA - BIG BROTHER (or, on the ethical and moral aspects of artificial intelligence).

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Book SynopsisThis book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.In recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with some kind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.Primarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use. Table of ContentsIntroduction.- Topological foundations, hypercomplexes and homology.- Weighted complexes, cohomology and Laplace operators.- The Laplace operator and the geometry of graphs.- Metric spaces and manifolds.- Linear methods: Kernels, variations, and averaging.- Nonlinear schemes: Clustering, feature extraction and dimension reduction.- Manifold learning, the scheme of Laplacian eigenmaps.- Metrics and curvature.

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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

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Book SynopsisUnderstanding AWS ML services, including SageMaker, Lambda, Glue, and other ML tools. Design secure S3, EFS, and EBS repositories, implement data ingestion solutions, and perform data transformation. Frame business problems; select supervised, unsupervised, or ensemble models.

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Book SynopsisThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

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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 Synopsis Luca Antiga is a deep learning researcher and entrepreneur known for translating theory into high-impact AI applications. With extensive industry and academic collaborations, Luca brings clarity and pragmatic rigor to every page. He distills years of neural-network innovation into guidance that accelerates reader competence.  Eli Stevens is a seasoned machine-learning engineer recognized for simplifying complex architectures for production teams. With startup intensity and open-source spirit, Eli delivers frank, actionable insights throughout the book. He converts frontier research into approachable steps that help readers deploy real-world solutions.  Howard Huang is a software engineer on the core PyTorch team, known for advancing distributed training at scale. With insider knowledge of the framework, Howard injects authoritative best practices into the narrative. He turns deep infrastructure expertise into clear tactics that boost reader productivity.  Thomas Viehmann is a data-science consultant and educator praised for demystifying advanced AI concepts. With classroom experience and community mentorship, Thomas offers an encouraging, structured teaching style. He translates academic depth into tools and patterns readers can apply immediately. 

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisDiscover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning's common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how to: Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization Use unsupervised learning with a deep learning autoencoder to regenerate sample data Understand the basics of reinforcement learning and the Q Learning equation Apply Q Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. about the technology Evolutionary deep learning merges the biology-simulating practices of evolutionary computation (EC) with the neural networks of deep learning. This unique approach can automate entire DL systems and help uncover new strategies and architectures. It gives new and aspiring AI engineers a set of optimization tools that can reliably improve output without demanding an endless churn of new data. about the reader For data scientists who know Python.

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

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Book SynopsisThis practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field.

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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 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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