Machine learning

This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.¿Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.¿ -- Geoffrey Hinton"With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." ¿ Yann LeCun¿This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.¿ -- Yoshua Bengio

Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Gain a competitive edge in today's data-driven world and build a rich career as a data professional that drives business success and innovation...Did you know that every minute, people around the world make 5.9 million searches on Google, share 1.7 million posts on Facebook, and watch 1 million hours of videos? And that's not even all of it! In total, the world creates a massive 328 million terabytes of data every day!Data is everywhere... and it has become the essential building block of this modern society, shaping the future of businesses, technology, and human interactions.It's no wonder that data professional roles, such as data analysts, data scientists, and data architects are now highly in demand in this data-driven world. And that's why now is the perfect time to pursue a career in data.But what does it take to become a competent data professional? This book has all the answers!Building a successful career in data is all about cultivating the necessary mindset, knowledge, and skills. This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems.Here is just a fraction of what you will discover:A comprehensive guide to the data analytics process - a beginner-friendly 5-step framework to kickstart your journey into analyzing and processing dataHow to get started with the fundamental concepts, theories, and models for accurately analyzing dataEverything you ever needed to know about data mining and machine learning principles - your gateway to unlocking the secrets of data's hidden potentialWhy business run on a data-driven culture, and how you can leverage it using real-time business intelligence analyticsStrategies and techniques to build a problem-solving mindset that can overcome any complex and unique datasetHow to create compelling and dynamic visualizations that help generate insights and make data-driven decisionsThe 4 pillars of a new digital world - discover how emerging technologies will transform the landscape of analyzing dataAnd much more.Believe it or not, you can be terrible in math or statistics and still pursue a career in data. Yes, you need a good grasp of the basics but always keep in mind that knowledge can be acquired and skills can be developed.The key is to not be intimidated by the strings of characters and numbers. Focus, instead, on building a mindset that thirsts for knowledge. You'll be surprised by how much you're able to do with just that. And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you.So, what are you waiting for? Ready to master the fundamentals and build a successful career in data analytics?

This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI.Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. Yoüll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.What You Will LearnGain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google BardImplement large language models using Sklearn, complete with code examples and best practices for real-world applicationLearn how to integrate LLM¿s in enterprises, including aspects like LLMOps and technology stack selectionUnderstand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insightsWho This Book Is ForData scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.

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

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey Features:Implement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description:Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What You Will Learn:Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is for:This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?

A self-contained and practical introduction that assumes no prior knowledge of programming or machine learning.

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

Engineering Deep Learning Systems teaches you to design and implement an automated platform to support creating, training, and maintaining deep learning models. In it, you'll learn just enough about deep learning to understand the needs of the data scientists who will be using your system. You'll learn to gather requirements, translate them into system component design choices, and integrate those components into a cohesive whole. A complete example system and insightful exercises help you build an intuitive understanding of DL system design.

Explore the capabilities of ChatGPT and gain insight on how to utilize this AI tool in your daily tasks, and marketing endeavors. This book introduces ChatGPT, covering its architecture, training process, and applications across various fields.Start by delving into the benefits of integrating ChatGPT into everyday routines, emphasizing its potential to streamline tasks, optimize time management, and provide valuable insights that can revolutionize individuals¿ work approaches. You'll then look more closely at ChatGPT¿s mechanics, its capabilities, limitations, and unique features. The book also outlines the best practices for utilizing ChatGPT, offering practical tips, techniques, and strategies to enhance output quality and reliability, while minimizing errors and maximizing results.Yoüll focus on ChatGPT¿s relevance in marketing tasks, such as generating product descriptions, creating email templates, automating social media posts, and addressing customer inquiries. The book concludes by exploring techniques for marketing with ChatGPT, including integration with other tools, data analysis, reporting, and customizing ChatGPT to meet specific marketing needs. In the end, yoüll have the knowledge and skills needed to leverage ChatGPT¿s AI marketing capabilities and to harness its power for success in the digital age.What Yoüll Learn Understand the concepts and workings of ChatGPT, its architecture, and the training process Apply the best practices for ChatGPT Create email templates and automate social media posts using ChatGPT Use ChatGPT for data analysis and reportingWho This Book Is ForMarketing professionals, business owners and entrepreneurs, content creators, and customer service representatives

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. This monograph provides an expansive perspective on what constitutes an automated/autonomous ML system. In doing so, the authors survey developments in hyperparameter optimisation, multicomponent models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. Furthermore, they develop a conceptual framework throughout to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. This monograph lays the groundwork for students and researchers to understand the factors limiting architectural integration, without which the field of automated ML risks stifling both its technical advantages and general uptake.

Uncover the secrets of Big Data with our comprehensive book bundle: "Big Data: Statistics, Data Mining, Analytics, and Pattern Learning." Dive into the world of data analytics and processing with Book 1, where you'll gain a solid understanding of the fundamentals necessary to navigate the vast landscape of big data.In Book 2, explore data mining techniques that allow you to extract valuable insights and patterns from large datasets. From marketing to finance and beyond, discover how to uncover hidden trends that drive informed decision-making.Ready to take your skills to the next level? Book 3 delves into advanced data science, where you'll learn to harness the power of machine learning for big data analysis. From regression analysis to neural networks, master the tools and techniques that drive predictive modeling and pattern recognition.Finally, in Book 4, learn how to design robust big data architectures that can scale to meet the needs of modern enterprises. Explore architectural patterns, scalability techniques, and fault tolerance mechanisms that ensure your systems are resilient and reliable.Whether you're a beginner looking to build a solid foundation or an experienced professional seeking to deepen your expertise, this book bundle has something for everyone. Don't miss out on this opportunity to unlock the potential of Big Data and drive innovation in your organization. Order now and embark on your journey to becoming a Big Data expert!

The fusion of the Internet of Things (IoT) with the prowess of Machine Learning and Deep Learning has ignited a revolution in the realm of technology. "IoT Application of Machine Learning and Deep Learning" is a comprehensive journey into this transformative synergy, designed to illuminate the convergence of these cutting-edge domains. This book aims to be a guiding light for enthusiasts, practitioners, and scholars navigating the dynamic landscape where IoT's interconnected devices meet the intelligence of machine and deep learning algorithms.In these pages, readers will embark on an expedition through the myriad applications, breakthroughs, and evolving possibilities of leveraging machine and deep learning within the IoT ecosystem. From unraveling the intricate mechanisms behind predictive analytics in IoT to exploring the robustness of neural networks in enhancing IoT security, this book unveils the intricate tapestry of possibilities awaiting exploration. Whether you're a seasoned professional seeking deeper insights or an eager learner venturing into this interdisciplinary realm, this book endeavors to be a beacon, illuminating the boundless potential at the nexus of IoT, Machine Learning, and Deep Learning.

Welcome to the world of AI and Deep Learning! This book is a culmination of years of research, innovation, and practical insights into the fascinating realms of artificial intelligence and deep learning. In this rapidly evolving landscape, the convergence of neuroscience, mathematics, and computer science has sparked a revolution, redefining how machines perceive, learn, and interact with the world. As you embark on this educational journey, you'll delve into the foundational principles that underpin these technologies, unraveling complex concepts in a lucid and accessible manner. Whether you're an aspiring data scientist, a curious enthusiast, or a seasoned professional seeking to expand your knowledge, this book aims to equip you with a solid understanding of AI and deep learning essentials, empowering you to navigate the frontiers of innovation and contribute meaningfully to this transformative field.In this comprehensive guide, we traverse the landscape of artificial intelligence and deep learning, demystifying intricate theories and methodologies. From the fundamentals of neural networks to the practical applications in image recognition, natural language processing, and beyond, each chapter is meticulously crafted to provide a holistic view of these cutting-edge technologies. With a blend of theoretical foundations, real-world examples, and hands-on exercises, this book is designed to foster a deep comprehension of the core concepts while igniting your creativity to explore and innovate. Join us on this immersive journey, where you'll not only grasp the essence of AI and deep learning but also cultivate the skills to create intelligent systems that can revolutionize industries, redefine possibilities, and shape the future.

This monograph reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI.

This open access book offers comprehensive, self-contained knowledge on Digital Twin (DT), which is a very promising technology for achieving digital intelligence in the next-generation wireless communications and computing networks. DT is a key technology to connect physical systems and digital spaces in Metaverse.The objectives of this book are to provide the basic concepts of DT, to explore the promising applications of DT integrated with emerging technologies, and to give insights into the possible future directions of DT. For easy understanding, this book also presents several use cases for DT models and applications in different scenarios.The book starts with the basic concepts, models, and network architectures of DT. Then, we present the new opportunities when DT meets edge computing, Blockchain and Artificial Intelligence, anddistributed machine learning (e.g., federated learning, multi-agent deep reinforcement learning). We also present a wide application of DT as an enabling technology for 6G networks, Aerial-Ground Networks, and Unmanned Aerial Vehicles (UAVs).The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of DT. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists.

The mathematical theory of tangles, the centrepiece of the celebrated Robertson-Seymour theory of graph minors, finds precise structure in imprecise data. Assuming only basic undergraduate mathematics, this book shows how tangles can identify, relate, and structure types in data: of behaviour, political views, texts, or proteins.

This book constitutes the revised selected papers of the First International Conference, ICCSST 2023, held in Bangalore, India, during May 8¿9, 2023.The 39 full papers included in this volume were carefully reviewed and selected from 200 submissions. They focus on artificial intelligence, blockchain technology, cloud computing, cyber security, data science, e-commerce, computer architecture, image and video processing, pandemic preparedness and digital technology, pattern recognition and classification.

'Generativ AI: 17 vinkler til forståelse og anvendelse' er en lærerig bog, der åbner døren til en spændende verden af generativ kunstig intelligens. Skrevet af to brødre med passion for AI, tager denne bog dig med på en rejse fra teori til praksis, og fra nysgerrighed til forståelse af generativ AI.Med et særligt fokus på populære sprogmodeller som ChatGPT og tekst-til-billede værktøjer, giver bogen dig indblik i, hvordan generativ AI kan påvirke vores måde at tænke, skabe og interagere med teknologi på. Struktureret i 17 uafhængige 'vinkler', dækker bogen et bredt spektrum af emner - fra grundlæggende principper til diverse anvendelsesmetoder.Ud over at give dig en teoretisk forståelse, baseret på den nyeste forskning på området, får du også en praktisk håndbog, der kan tage din brug af AI til et højere niveau. Uanset om du er nybegynder eller erfaren bruger, giver bogen dig værktøjerne og viden til selv at udforske og anvende generativ AI, hvor end du ønsker.Du kan læse mere om bogen på hjemmesiden:https://momentum-ai.dk/bog/

The recent surge of interest in AI systems has raised concerns in moral quarters about their ethical use and whether they can demonstrate fair decision taking processes. Issues of unfairness and discrimination are pervasive when decisions are being made by humans, and are potentially amplified when decisions are made using machines with little transparency, accountability, and fairness. In this monograph, the authors introduce a framework for causal fairness analysis to understand, model, and possibly solve issues of fairness in AI decision-making settings. The authors link the quantification of the disparities present in the observed data with the underlying, often unobserved, collection of causal mechanisms that generate the disparity in the first place, a challenge they call the Fundamental Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, they study the mapping variations and empirical measures of fairness to structural mechanisms and different units of the population, culminating in the Fairness Map.This monograph presents the first systematic attempt to organize and explain the relationship between various criteria in fairness and studies which causal assumptions are needed for performing causal fairness analysis. The resulting Fairness Cookbook allows anyone to assess the existence of disparate impact and disparate treatment. It is a timely and important introduction to developing future AI systems incorporating inherent fairness and as such will be of wide interest not only to AI system designers, but all who are interested in the wider impact AI will have on society.

Fusion: Blending disciplines for deeper understandingFusion is the process of blending two or more different disciplines. It is a creative process that can be used to create something new and different. Fusion can help to promote dialogue between different ideas, perspectives, or cultures.The need for fusionThe world is changing rapidly. New technologies, ideas, and cultures are constantly emerging. In order to understand and effectively respond to these changes, we need to learn how to blend different disciplines.Fusion can help us to create new understandings. It allows us to identify the connections between two different things and use them in new ways.Examples of fusionFusion is used in many fields. Here are a few examples:· Technology: Fusion is used to develop new technologies. For example, solar fusion is a new method for generating solar energy more efficiently.· Education: Fusion is used to help students understand the connections between different subjects. For example, fusion between science and the arts can help students to think creatively and solve problems.

¿This book constitutes the proceedings of the 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023, as well as the Cardiac MRI Reconstruction Challenge, CMRxRecon Challenge. There was a total of 53 submissions to the workshop. The 24 regular workshop papers included in this volume were carefully reviewed and selected from 29 paper submissions. They deal with cardiac segmentation, modelling, strain quantification, registration, statistical shape analysis, and quality control. In addition, 21 papers from the CMRxRecon challenge are included in this volume. They focus on fast CMR image reconstruction and provide a benchmark dataset that enables the broader research community to promote advances in this area of research.

This book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques.The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on ¿social infrastructure¿ applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases.The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.