Machine learning Books

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 second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.

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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 SynopsisA practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models.Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author?s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. ?Integrates coverage of cutting-edge research, existing tools, and real-world applications?Provides students and professionals with an engineering view for production-ready machine learning systems?Proven in the classroom?Offers supplemental resources including slides, videos, exams, and further readings

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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 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 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 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 SynopsisNumerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart Proxy Models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations, which can otherwise take tens of hours. This book focuses on Smart Proxy Modeling and provides readers with all the essential details on how to develop Smart Proxy Models using Artificial Intelligence and Machine Learning, as well as how it may be used in real-world cases. Covers replication of highly accurate numerical simulations using Artificial Intelligence and Machine Learning Details application in reservoir simulation and modeling and computational fluid dynamics Includes real case studies based on commercially available simulators Table of Contents 1. Artificial Intelligence and Machine Learning. 2. Numerical simulation and modeling. 3. Proxy modeling. 4. Smart Proxy Modeling for numerical reservoir simulation. 5. Smart Proxy Modeling for computational fluid dynamics (CFD).

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

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

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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 SynopsisThis handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks.

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Book SynopsisSuccessful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML''s strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to learn complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specificaTrade Review'The book's excellent introduction explains why machine learning techniques will benefit asset managers substantially and why traditional or classical linear techniques have limitations and are often inadequate in asset management. It makes a strong case that ML is not a black box but a set of data tools that enhance theory and improve data clarity. López de Prado focuses on seven complex problems or topics where applying new techniques developed by ML specialists will add value.' Mark S. Rzepczynski, Enterprising InvestorTable of Contents1. Introduction; 2. Denoising and detoning; 3. Distance metrics; 4. Optimal clustering; 5. Financial labels; 6. Feature importance analysis; 7. Portfolio construction; 8. Testing set overfitting.

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Book SynopsisTable of ContentsForeword xv Introduction xix Chapter 1: Do You Want to Be Part of the Future? 1 Business at the Speed of AI 2 Follow Me, Follow Me 4 In AI, We Overtrust 6 Area 52 Ramblings 10 I’ll Do It 12 Adversarial Attacks Are Happening 16 ML Systems Don’t Jiggle-Jiggle; They Fold 19 Never Tell Me the Odds 22 AI’s Achilles’ Heel 25 Chapter 2: Salt, Tape, and Split-Second Phantoms 29 Challenge Accepted 30 When Expectation Meets Reality 35 Color Me Blind 39 Translation Fails 42 Attacking AI Systems via Fails 44 Autonomous Trap 001 48 Common Corruption 51 Chapter 3: Subtle, Specific, and Ever-Present 55 Intriguing Properties of Neural Networks 57 They Are Everywhere 60 Research Disciplines Collide 62 Blame Canada 66 The Intelligent Wiggle-Jiggle 71 Bargain-Bin Models Will Do 75 For Whom the Adversarial Example Bell Tolls 79 Chapter 4: Here’s Something I Found on the Web 85 Bad Data = Big Problem 87 Your AI Is Powered by Ghost Workers 88 Your AI Is Powered by Vampire Novels 91 Don’t Believe Everything You Read on the Internet 94 Poisoning the Well 96 The Higher You Climb, the Harder You Fall 104 Chapter 5: Can You Keep a Secret? 107 Why Is Defending Against Adversarial Attacks Hard? 108 Masking Is Important 111 Because It Is Possible 115 Masking Alone Is Not Good Enough 118 An Average Concerned Citizen 119 Security by Obscurity Has Limited Benefit 124 The Opportunity Is Great; the Threat Is Real; the Approach Must Be Bold 125 Swiss Cheese 130 Chapter 6: Sailing for Adventure on the Deep Blue Sea 133 Why Be Securin’ AI Systems So Blasted Hard? An Economics Perspective, Me Hearties! 136 Tis a Sign, Me Mateys 141 Here Be the Most Crucial AI Law Ye’ve Nary Heard Tell Of! 144 Lies, Accursed Lies, and Explanations! 146 No Free Grub 148 Whatcha measure be whatcha get! 151 Who Be Reapin’ the Benefits? 153 Cargo Cult Science 155 Chapter 7: The Big One 159 This Looks Futuristic 161 By All Means, Move at a Glacial Pace; You Know How That Thrills Me 163 Waiting for the Big One 166 Software, All the Way Down 169 The Aftermath 172 Race to AI Safety 173 Happy Story 176 In Medias Res 178 Big-Picture Questions 181 Acknowledgments 185 Index 189

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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 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 SynopsisIn this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.

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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 SynopsisIn this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed.

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Book Synopsis"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technologyDeep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the bookInside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the readerFor Python programmers with basic machine learning skills. about the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.Trade Review“Afantastic book with a colourful and intuitive way of describing how deep learning works.” Richard Vaughan “Amazing at what it does. It's a book for people who not only want to use deep learning, but also understand it!” Adam Slysz “A remarkably clear explanation of practical deep learning showing readers how to quickly and systematically apply deep learning techniques tosolve their everyday data problems.” Jeff Neumann “If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!” Tiklu Ganguly “A must read if you don't understand how Deep Learning works under the hood.” Abdul Basit Hafeez

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Book SynopsisSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

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Book SynopsisWINNER: Independent Press Awards 2023 - Category: Technology AI presents a new paradigm in software development, representing the biggest change to how we think about quality and testing in decades. Many of the well known issues around AI, such as bias, manifest themselves as quality management problems. This book, aimed at testing and quality management practitioners who want to understand more, covers trustworthiness of AI and the complexities of testing machine learning systems, before pivoting to how AI can be used itself in software test automation.Trade ReviewA brilliant reference with a focus on introducing the reader to new AI ideas and challenges. The danger with AI and software testing is the mistaken belief that people understand it all. This book addresses this issue by opening the reader up to a rich source of references & useful concepts using use cases, models and references, to both stimulate and challenge the reader's own knowledge of this broad subject. Highly recommended. -- Paul Mowat MBCS CITP, BCS SiGIST Social Media Secretary & Committee Member, Quality Test Engineer Director, Deloitte UKAI-based systems conquer more and more areas of our daily life. People are concerned whether these systems are trustworthy. 'Artificial Intelligence and Software Testing' tackles this issue and provides an insight into AI quality and how it differs from conventional software quality, and where the difficulties and challenges are in testing machine learning systems. A great introduction into this topic and must read for all interested in building AI-based systems that you can trust. -- Klaudia Dussa-Zieger, Chair GTB & Vice President ISTQB®, Head of ISTQB® Certified Tester AI Testing (CT-AI) taskforceIn the ever expansive and evolving virtual domain, the prominence of AI is becoming more and more prolific, and this evolution will not be without its challenges. This title provides an excellent resource into the potential dilemmas faced in this evolutionary field as the virtual, cognitive, and physical spaces become more interlinked with the dawn of the metaverse. The part that humans play in the growth, development and testing of AI is discussed. Supported by a wealth of experience, research, and evidence from the authors, the title provides a great introduction to and understanding of AI and software testing. Highly recommended for all with an interest in this area. -- Jonathan Miles MBA BSc(Hons) FCMI, Head of Strategic Intelligence, MimecastShift Right! A concept you won’t find in ‘The Seven Principles of Testing’. 'Artificial Intelligence and Software Testing' puts the principles into perspective. Not only does it explore early testing, but it also looks at the concept of exhaustive testing thoroughly and effectively. As a trainer of software testing I will definitely use all this book has to offer. Guiding the next generation of testers to question the intricacies of machine learning. A must for anyone in tech, not just software testers. -- Rachel Hurley MBCS TAP.dip, Technical Trainer (Software Testing)As the title describes, this book is a robust AI and ML testing exploration that also dives into the juxtaposition of the trustworthiness and bias in AI systems. It touches on the basis of ontologies and how to enable the considerable impact of testing and monitoring of AI-based systems. After reading this you would be able to answer an important challenge: how to determine that your AI system has been extensively tested? -- Dina Dede, AI/ML and Cloud Architect Lead, UKThis book beautifully captures the game-changing complexity of artificial intelligence (AI) and the traditional discipline of software quality management. It is a comprehensive manual addressing the conundrum and tantalizing promise of both disciplines with good pace and a distinct future-present context. Forget waterfall and DevOps, we’re right shifting into OpsDev, AIOps and digital twins in the metaverse, so things are about to get a whole lot more interesting. Excellent effort, and a much-needed treatment of this topic by true experts. -- Jude Umeh FBCS CITP, Senior Program Architect, Salesforce'Artificial Intelligence and Software Testing' is a great read. The vast experience of the authors is evident as they comprehensively explain the challenges and benefits of not only applying AI to testing, but also testing the AI software itself. I found the insight into the shift-right approach and its application during the development of the test and trace application fascinating. A must read for any testing/QA professional plus any C-suite looking to rapidly increase their ROI on testing. -- Anil Pande, Managing Partner, TestPro Consulting LtdThis book is a very good introduction to using AI in software testing as well as testing AI systems, covering several relevant topics like societal risk, bias, ethical behavior, quality, trustworthiness, and the problems associated with AI/ML systems. I specifically liked the section that details on the problems associated with AI/ML systems. I would recommend this book to anyone who is starting their study on software testing vis-a-vis AI/ML systems. -- Venkat Ramakrishnan, Software Quality Leader And Software Testing Technologist'Artificial Intelligence and Software Testing' is a valuable resource for anyone curious in how to approach testing AI models as they expand into our daily lives. This is a clear, informative read which discusses within each chapter different testing challenges with AI software and advice on how to handle them effectively. I can highly recommend this to testers and students alike. -- Katy Hannath BSc(Hons), MSc in Artificial Intelligence and Data Science student, & Quality Assurance Tester, VISR DynamicsThis book is an exceptionally practical resource which is a remarkable reference guide to understanding the foundations of AI & ML for anyone wishing to build a career in AI or define a test approach. It has a clear, direct, and concise explanation of AI, ML, ethics, ontology, quality, bias, challenges, test automation, and the significance of ‘shift-right’ testing. It offers thorough, data-driven and real-world examples that bring together the rich wealth of experience from these expert authors and authorities in this area. -- Boby Jose BSc MBA MBCS, Author of BCS publication ‘Test Automation: A manager’s guide’What an exciting and relevant publication! Beyond the positive game-changing societal benefits delivered by AI, it has proven equally disruptive to all aspects of software engineering including software testing. This book provides great insight into new build and test design techniques to augment our traditional thinking. An essential guide for technology leaders and test professionals alike, looking to understand how to approach the critical problem of building and testing today’s complex and often unpredictable AI systems. -- Jack Mortassagne, Director at Cigniti Technologies and TMMI Accredited AssessorThis is a great book for those who want to gain more insight into how AI will affect the software testing profession. The writers introduce the challenges in AI in an easy-to-understand manner, while the case studies showcased are extremely interesting and contemporary, clearly exemplifying the topics presented. Brilliant read and highly recommended! -- Dr Diana Hintea BEng(Hons) PhD SFHEA, Associate Head of School (School of Computing, Electronics and Mathematics), Coventry UniversityIn a time the promised paradigm shift of Artificial intelligence is starting to have a real-world impact, this is a vitally important book. It explains the social, ethical, and technical concerns around AI in an easy to understand way, making a complex subject easily accessible. Everyone involved in IT is likely to be impacted by AI whether from a business, technical, ethical, or quality point of view and so this book will be an invaluable resource for everyone in IT. As a Testing and Quality specialist, this is going to have pride of place on my bookshelf as a practical, real-world reference for helping me navigate testing and quality in the emerging world of AI. -- Bryan Jones MBCS, Director of Testing Practice, Sopra Steria Private SectorThis book is a must-read for anyone in software testing with responsibility for quality assuring AI technology that must engender public trust. With topics that feel both familiar and challenging, the authors confidently explore a range of subjects to broaden and deepen the reader’s understanding of the intersection of AI and testing. -- Bronia Anderson-Kelly, IT change consultant, Sabiduria LtdThis book addresses an often ignored but critically important aspect of AI implementation: how to ensure that AI's are producing, and continue to produce, acceptable output. As AI is non-deterministic and complex, and dataset quality can be highly variable, it is notoriously difficult to determine suitable test cases for modern systems. In this book, the authors provide practical methods and examples that can be used to ensure AI quality, and as such is an extremely useful resource for anyone implementing systems involving AI and machine learning. -- Peter Brightwell MSc, Intelligent Automation Architect, NDL SoftwareTable of Contents Introduction AI Trustworthiness and Quality Quality and Bias Testing Machine Learning Systems AI-based Test Automation Ontologies for Software Testing Shifting Right into the Metaverse with Digital Twin Testing

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Book SynopsisMaking use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.Table of ContentsIntroduction Practical Data Analysis: An Example Project Understanding Data Understanding Principles of Modeling Data Preparation Finding Patterns Finding Explanations Finding Predictors Evaluation and DeploymentThe Labelling Problem Appendix A: Statistics Appendix B: KNIME

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Book SynopsisThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. Table of Contents1. Data Science Process2. Dimensionality Reduction - Creating Manageable Training Datasets3. Classical Algorithms - Overview4. Regression Analysis5. Decision Tree6. Ensemble - Bagging and Boosting7. K-Nearest Neighbors8. Naive Bayes9. Support Vector Machines: A supervised learning algorithm for Classification and Regression10. Clustering Overview11. Centroid-based Clustering12. Connectivity-based Clustering13. Gaussian Mixture Model14. Density-based15. BIRCH16. CLARANS17. Affinity Propagation Clustering18. STING19. CLIQUE20. Artificial Neural Networks21. ANN-based Applications22. Automated Tools23. Data Scientist’s Ultimate Workflow

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Book SynopsisThis textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.Table of Contents1. Basics of Modern Computer Systems (Unix/Linux Centric) a. Computer Hardware Basics b. Operating System c. Files & Directories d. Programs and Shells e. Programming Languages f. Troubleshooting Computer Problems (How to Google issues.) 2. The Python Programming Language ( A tool to enter the world of Machine Learning.) a. Short History b. The Python Interpreter c. Basic Syntax d. Working with Popular Libraries (Modules) e. Optimization f. Advanced Concepts g. Introduction to ML libraries 3. Basic Math a. Overview b. Linear Algebra Basics c. Calculus Basics d. Probability e. Use cases of above three in ML 4. Introduction to the World of Bioinformatics a. Laying the Foundation b. A Brief History c. Goals of Bioinformatics d. Genomes, Genes, Sequences e. Protein and structures f. Databases g. Bioinformatics Tools 5. Introduction to Artificial Intelligence & ML a. Machine Learning i. History of Machine Learning ii. Why Machine Learning? iii. Machine Learning Approaches iv. Machine Learning Applications b. Artificial Intelligence i. What is AI? ii. Basic Principles iii. General applications of Artificial Intelligence 6. Fundamentals of ML a. Types of learning i. Supervised ii. Unsupervised b. Popular Algorithms c. Deep learning and related concepts d. Model Training and Testing e. Summary 7. Applications in the field of Bioinformatics a. In Systems Biology b. Biological Data Analysis and Predictions c. In Healthcare, Diagnosis and Treatment 8. Future Prospects 9. Further Readings

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Book SynopsisChapter 1 Introduction.- Chapter 2 Learning and Decision Making Process.- Chapter 3 Fuzzy Logic Inference System.- Chapter 4 Introduction to Machine Learning.- Chapter 5 Introduction to Regression Algorithms.- Chapter 6 Introduction to Classification Algorithms.- Chapter 7 Neural Networks and Deep Learning.- Chapter 8 Introduction to Unsupervised Learning.- Chapter 9 Introduction to Reinforcement Learning.- Chapter 10 Introduction to Adaptive Neuro Fuzzy Inference System.- Chapter 11 Case Study Projects on Fuzzy Logic Technology.- Chapter 12 Case Study Projects on Deep Learning.- Appendix A.

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Book SynopsisThis book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance. It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for deep learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques. Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting. They will also benefit from deep learning by optimizing models for image processing, natural language processing, and specialized applications. The reader can put theory into practice with hands-on case studies and code examples, reinforcing their understanding.

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Book SynopsisVolume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.

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Book SynopsisThis set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided. The third is an explanation of the techniques and methods used in data visualization with R, which can be used to communicate the results collected by the AI systems when they are analyzed statistically. Practice questions are provided throughout the book.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisThis book explores the significance, challenges and benefits of digital twin technologies; it focuses in particular on various architectures, applications and challenges in the implementation of digital twins to Machine Learning and Internet of Things capabilities. Through the analysis of smart city and smart manufacturing case studies, the book explores the benefits of digital technologies in the Industry 4.0 Era.

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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 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 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 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 Synopsis Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You''ll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You''ll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.  Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticTable of Contents CHAPTER 1: Overview of Python Language 1.1 Philosophy of Python programming 1.2 Comparison with other languages 1.4 Design patterns in Python 1.4.1 Structural patterns 1.4.2 Behavioral patterns 1.4.3 Creational patterns 1.5 Why Python is so popular? 1.6 Use-case where Python does not fit well 1.7 Interfacing Python with other languages 1.7.1 Running Stanford NLP Java library in Python 1.7.2 Running time series Holt- Winter R module in Python 1.7.3 Expose your Python program as service in 2 minutes 1.8 Essential architectural pattern in data analytics 1. Hot Potato anti pattern 2. Data collector as a service 3. Bridge & proxy patterns. 4. Application layering CHAPTER 2: ETL with Python 2.1 Introduction 2.2 Python &Mysql 2.3 Python & Neo4j 2.4 Python & Elastic Search 2.5 Crawling with Beautiful Soup 2.6 Crawling using selenium 2.7 Regular expressions 2.8 Panda framework 2.9 Cloud Storages 2.9.1 AWS storage 2.10.1 GCP storages 2.9 Topical crawling 2.9.1 Find potential activists for a political party from web CHAPTER 3: Supervised Learning and Unsupervised Learning with Python 3.1. Introduction 3.2 Correlation analysis 3.2.1 Measures of correlation 3.2.2 Threshold for correlation 3.2.3 Dealing uneven cordiality of features 3.3 Principle component analysis 3.3.1 Singular value decomposition algorithm 3. 3.2 Factor analysis 3.3.3 Use case: Measuring impact of change in organization 3.4 Mutual information & dealing with categorical data 3.4.1 Use case: Measuring most significant features in ad price prediction 3.5 Feature engineering in texts and images 3.5.1 Classification 3. 5.2 Decision tree & entropy gain 3. 5.3 Random forest classifier 3. 5.4 Naïve bay’s classifier 3. 5.5 Support vector machine 3. 5.6 Text classification using Python 3. 5.7 Image classification using Python 3. 5.8 Supervised & unsupervised learning 3. 5.9. Semi supervised learning 3. 6.1 Regression 3. 6.2 Least-square estimation 3. 6.3 Logistic regression 3. 6.4 Classification using regression 3.6.5 Feature scaling 3.6.6 Intentionally bias the model to over fit or under fit CHAPTER 4: Clustering with Python 4.1 Introduction 4.2 Distance measures 4.3 Hierarchical clustering 4.3.1 Top to bottom algorithm 4.3.2 Bottom to top algorithm 4.3.3 Dendrogram to cluster 4.3.4 Choosing the threshold 4.4 K-Mean clustering 4.4.1 Algorithm 4.4.2 Choosing K 4.5 Graph theoretic approach 4.6 Measure for good clustering 4.7 Find summary of a paragraph 4.8 Find faces in images CHAPTER 5: Deep Learning & Neural Networks 5.1 History 5.2 Architecture 5.3 Use-case where NN fit well 5.4 Back propagation algorithm 5.5 Quick tour to other NN algorithms 5.6 Regularization techniques 5.7 Recurrent neural network 5.8 Goal oriented dialog system 5. 9.1 Convolution neural network 5. 9.2 Fake image detection Introduction to reinforcement learning 1. Dancing Floor on GCP 2. Dialectic Learning CHAPTER 6: Time Series Analysis 6.1 Introduction 6.2 Smoothing techniques 6.3 Autoregressive model 6.4 Moving average model 6.5 ARMA model 6.6 ARIMA model 6.7. SARIMA model 6.8 Historical practice 6.9 Frequency domain analysis in time series CHAPTER 7: Analytics in Scale 7.1 Introduction 7.2 Hadoop architecture 7.3 Popular design pattern in MapReduce 7.4 Introduction to cloud 7.5. Analytics on cloud 7.6 Introduction to Spark 7.7. Spark architecture - Memory optimization - Problem with memory optimization - Essential parameter in Spark - Naïve Bayes classifier in Spark 7.8 A recommendation system in Spark

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Book SynopsisThis book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.Table of ContentsAn Introduction to Neural Networks.- The Backpropagation Algorithm.- Machine Learning with Shallow Neural Networks.- Deep Learning: Principles and Training Algorithms.- Teaching a Deep Neural Network to Generalize.- Radial Basis Function Networks.- Restricted Boltzmann Machines.- Recurrent Neural Networks.- Convolutional Neural Networks.- Graph Neural Networks.- Deep Reinforcement Learning.- Advanced Topics in Deep Learning.

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