Instant New York Times and USA Today Bestseller. Tech visionary and co-founder of Manas AI Reid Hoffman shares his unique insider’s perspective on an AI-powered future, making the case for its potential to unlock a world of possibilities. "An essential companion." — Fei-Fei Li "An important read." —Bill Gates "Brilliant mind. Compassionate heart. Bold ideas…Read this book!” —Van Jones "Refreshingly optimistic and welcome perspective." — Ariana Huffington "A fascinating and insightful book." —Yuval Noah Harari As taught at UPenn's Wharton and Stanford.Superagency offers a roadmap for using AI inclusively and adaptively to improve our lives and create positive change. While acknowledging challenges like disinformation and potential job changes, the book focuses on AI’s immense potential to increase individual agency and create better outcomes for society as a whole. Imagine AI tutors personalizing education for each child, researchers rapidly discovering cures for diseases like Alzheimer's and cancer, and AI advisors empowering people to navigate complex systems and achieve their goals. Hoffman and co-author, tech and culture writer Greg Beato envision a world where these possibilities, and many more, become a reality. Superagency challenges conventional fears, inviting us to view the future through a lens of opportunity, rather than fear. It’s a call to action – to embrace AI with excitement and actively shape a world where human ingenuity and the power of AI combine to create something extraordinary. Entrepreneur Reid Hoffman is a co-founder of LinkedIn, Inflection AI, and Manas AI. He is also host of the podcasts Possible and Masters of Scale. Read more

System Design Interview - An Insider's Guide (Volume 2)This book can be seen as a sequel to the book: System Design Interview - An Insider’s Guide. It covers a different set of system design interview questions and solutions. Although reading Volume 1 is helpful, it is not required. This book should be accessible to readers who have a basic understanding of distributed systems.This volume provides a reliable strategy and knowledge base for approaching a broad range of system design questions that you may encounter. It will help you feel confident during this important interview. This book provides a step-by-step framework for how to tackle a system design question. It also includes many real-world examples to illustrate a systematic approach, with detailed and well-explained steps you can follow.What’s inside?- An insider’s take on what interviewers really look for and why.- A 4-step framework for solving any system design interview question.- 13 real system design interview questions with detailed solutions.- 300+ diagrams to visually explain how different systems work.Table of ContentsChapter 1: Proximity ServiceChapter 2: Nearby FriendsChapter 3: Google MapsChapter 4: Distributed Message QueueChapter 5: Metrics MonitoringChapter 6: Ad Click Event AggregationChapter 7: Hotel ReservationChapter 8: Distributed Email ServiceChapter 9: S3-like Object StorageChapter 10: Real-time Gaming LeaderboardChapter 11: Payment SystemChapter 12: Digital WalletChapter 13: Stock Exchange Read more

Raspberry Pi devices are perfect for experimenting with neural networks, artificial intelligence, and machine learning.Even without additional accelerator hardware, you can enhance your Raspberry Pi 4, 5, and Pico projects with AI However, if you combine the Raspberry Pi 5 with the Raspberry Pi AI Kit, you can build complex AI vision applications, running in real time, with low latency and low power requirements.This book shows you how to get up and running with pretrained models or models you train yourself. You'll learn how to work with models that can:Identify and categorise objects from pictures or videoEstimate the pose of a person, animal, or robotRecognise speech or generate speech from textTranslate from one language to anotherDetect unusual changes in the environment with temperature, light, and other sensorsWhen you combine Raspberry Pi with artificial intelligence, it gives you the power to create all sorts of things: robots that can interact intelligently with their surroundings, games that use the physical world as a playing field, and home automation systems that adjust to the environment in real time. With this guide, you'll be up and running with your own Raspberry Pi-powered applications in no time! Read more

THE #2 TIMES BESTSELLER'Artificial intelligence is going to have a massive impact on everyone’s lives... an accessible and sensible read that helps demystify AI' Deborah Meaden, entrepreneur and star of Dragon's Den'Nigel Toon is a visionary leader in the field of artificial intelligence... a must-read' Marc Tremblay, Distinguished Engineer, MicrosoftThose who understand how AI thinks are about to win big.We are used to thinking of computers as being a step up from calculators - very good at storing information, and maybe even at playing a logical game like chess. But up to now they haven't been able to think in ways that are intuitive, or respond to questions as a human might. All that has changed, dramatically, in the past few years.Our search engines are becoming answer engines. Artificial intelligence is already revolutionising sectors from education to healthcare to the creative arts. But how does an AI understand sentiment or context? How does it play and win games that have an almost infinite number of moves? And how can we work with AI to produce insights and innovations that are beyond human capacity, from writing code in an instant to unfolding the elaborate 3D puzzles of proteins?We stand at the brink of a historic change that will disrupt society and at the same time create enormous opportunities for those who understand how AI thinks. Nigel Toon shows how we train AI to train itself, so that it can paint images that have never existed before or converse in any language. In doing so he reveals the strange and fascinating ways that humans think, too, as we learn how to live in a world shared by machine intelligences of our own creation. Read more

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The Gang of Four’s seminal catalog of 23 patterns to solve commonly occurring design problemsPatterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves. Highly influential, Design Patterns is a modern classic that introduces what patterns are and how they can help you design object-oriented software and provides a catalog of simple solutions for those already programming in at last one object-oriented programming language.  Each pattern:Describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger designIs compiled from real systems and based on real-world examplesIncludes downloadable C++ source code that demonstrates how patterns can be implemented and PythonFrom the preface: “Once you the design patterns and have had an ‘Aha!’ (and not just a ‘Huh?’) experience with them, you won't ever think about object-oriented design in the same way. You'll have insights that can make your own designs more flexible, modular, reusable, and understandable - which is why you're interested in object-oriented technology in the first place, right?” Read more

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.Use Scikit-learn to track an example ML project end to endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning Read more

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. In addition, our readers are given free access to online e-Chapters that we update with the current trends in Machine Learning, such as deep learning and support vector machines. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. What we have emphasized are the necessary fundamentals that give any student of learning from data a solid foundation. The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions. Read more

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

Be at your A game in building Intelligent systems by leveraging Computer vision and Machine Learning.Key Features ● Step-by-step instructions and code snippets for real world ML projects.● Covers entire spectrum from basics to advanced concepts such as deep learning, transfer learning, and model optimization● Loaded with practical tips and best practices for implementing machine learning with OpenCV for optimising your workflow.Book DescriptionThis book is an in-depth guide that merges machine learning techniques with OpenCV, the most popular computer vision library, using Python. The book introduces fundamental concepts in machine learning and computer vision, progressing to practical implementation with OpenCV. Concepts related to image preprocessing, contour and thresholding techniques, motion detection and tracking are explained in a step-by-step manner using code and output snippets. Hands-on projects with real-world datasets will offer you an invaluable experience in solving OpenCV challenges with machine learning. It’s an ultimate guide to explore areas like deep learning, transfer learning, and model optimization, empowering readers to tackle complex tasks. Every chapter offers practical tips and tricks to build effective ML models. By the end, you would have mastered and applied ML concepts confidently to real-world computer vision problems and will be able to develop robust and accurate machine-learning models for diverse applications. Whether you are new to machine learning or seeking to enhance your computer vision skills, This book is an invaluable resource for mastering the integration of machine learning and computer vision using OpenCV and Python.What you will learn● Learn how to work with images and perform basic image processing tasks using OpenCV. ● Implement machine learning techniques to computer vision tasks such as image classification, object detection, and image segmentation. ● Work on real-world projects and datasets to gain hands-on experience in applying machine learning techniques with OpenCV. ● Explore the concepts of deep learning using Tensorflow and Keras and how it can be used for computer vision tasks. ● Understand the concept of transfer learning and how pre-trained models can be leveraged for new tasks. ● Utilize techniques for model optimization and deployment in resource-constrained environments. Who is this book for?This book is for everyone with a basic understanding of programming and who wants to apply machine learning in computer vision using OpenCV and Python. Whether you're a student, researcher, or developer, this book will equip you with practical skills for machine learning projects. Some familiarity with Python and machine learning concepts is assumed. Beginners too will find this book valuable as it offers clear examples and explanations for every concept.Table of ContentsChapter 1: Getting Started With OpenCV Chapter 2: Basic Image & Video Analytics in OpenCVChapter 3: Image Processing 1 using OpenCVChapter 4: Image Processing 2 using OpenCVChapter 5: Thresholding and Contour Techniques Using OpenCV Chapter 6: Detect Corners and Road Lane using OpenCVChapter 7: Object And Motion Detection Using OpencvChapter 8: Image Segmentation and Detecting Faces Using OpenCVChapter 9: Introduction to Deep Learning with OpenCVChapter 10: Advance Deep Learning Projects with OpenCVChapter 11: Deployment of OpenCV projectsAbout the AuthorThis is Mugesh S. I am working as a Data Scientist at Infosys, with a passion for leveraging data-driven insights to tackle complex challenges and drive business success. I am an engineering graduate who completed the PG program in Data Science and Engineering as well as a Master’s in Mathematics and Data Science, to deepen my understanding of the intricacies of data analytics. Read more

DEEP LEARNING FOR BEGINNERS: Get an accessible introduction to the artificial intelligence technology at the heart of the AI revolution—from driverless cars to speech recognition on mobile phones.Deep learning is an artificial intelligence technology that enables computer vision, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets. Its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures—including autoencoders, recurrent neural networks, and long short-term networks—as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—including major trends, possible developments, and significant challenges. Read more

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications. Read more

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment Read more

Learn to understand and implement the latest machine learning innovations to improve your investment performanceMachine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithmsConduct research with ML algorithms on big dataUse supercomputing methods and back test their discoveries while avoiding false positivesAdvances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance. Read more

The past decade has witnessed extraordinary advances in artificial intelligence. But what precisely is it and where does its future lie?In this brilliant, one-stop guide WIRED journalist Matt Burgess explains everything you need to know about AI. He describes how it works. He looks at the ways in which it has already brought us everything from voice recognition software to self-driving cars, and explores its potential for further revolutionary change in almost every area of our daily lives. He examines the darker side of machine learning: its susceptibility to hacking; its tendency to discriminate against particular groups; and its potential misuse by governments. And he addresses the fundamental question: can machines become as intelligent as human beings? Read more

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Read more

For Readers of Ray Kurzweil and Michio Kaku, a New Look at the Cutting Edge of Artificial Intelligence Imagine a robotic stuffed animal that can read and respond to a child’s emotional state, a commercial that can recognize and change based on a customer’s facial expression, or a company that can actually create feelings as though a person were experiencing them naturally. Heart of the Machine explores the next giant step in the relationship between humans and technology: the ability of computers to recognize, respond to, and even replicate emotions. Computers have long been integral to our lives, and their advances continue at an exponential rate. Many believe that artificial intelligence equal or superior to human intelligence will happen in the not-too-distance future; some even think machine consciousness will follow. Futurist Richard Yonck argues that emotion, the first, most basic, and most natural form of communication, is at the heart of how we will soon work with and use computers. Instilling emotions into computers is the next leap in our centuries-old obsession with creating machines that replicate humans. But for every benefit this progress may bring to our lives, there is a possible pitfall. Emotion recognition could lead to advanced surveillance, and the same technology that can manipulate our feelings could become a method of mass control. And, as shown in movies like Her and Ex Machina, our society already holds a deep-seated anxiety about what might happen if machines could actually feel and break free from our control. Heart of the Machine is an exploration of the new and inevitable ways in which mankind and technology will interact. The paperback edition has a new foreword by Rana el Kaliouby, PhD, a pioneer in artificial emotional intelligence, as well as the cofounder and CEO of Affectiva, the acclaimed AI startup spun off from the MIT Media Lab. Read more

Book Description:Introduction to TensorFlow Using Python provides an introduction to using TensorFlow with Python in easy bite sized pieces that any beginner or novice can understand.Filled with simple and straightforward working examples, the book aims to introduce the reader to the wonderful world of machine learning. Follow along for an adventure in coding and solving problems!What You Will Learn:How to use TensorFlowHow to create, train and use a machine learning model on dataHow to build and train Neural NetworksUnderstand how machine learning algorithms workClean, model and visualize data Read more

Beyond foundational LangChain documentation and LangGraph interfaces, learn enterprise patterns, key design pattern to build AI agents, battle-tested strategies, and proven architectures used in production. Ideal for Python developers building generative AI at scale. Key FeaturesGet to grips with building AI agents with LangGraphLearn about enterprise-grade testing, observability, and LLM evaluation frameworksCover RAG implementation with cutting-edge retrieval strategies and new reliability techniquesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis revised edition builds a foundation in agentic AI, LLM fundamentals, LangChain, & LangGraph for developers at all levels. Fully updated to cover the latest in LangChain and production LLM applications, it captures the evolving ecosystem and enterprise deployment landscape. New coverage includes multi-agent architectures, LangGraph interfaces, robust RAG techniques with hybrid search, re-rankers, and advanced fact-checking mechanisms, plus enterprise-grade testing frameworks. It provides coverage of key design patterns behind agentic systems, practical implementations of multi-agent systems for complex tasks. Explore cutting-edge agent strategies such as Tree of Thought, multi-agent orchestration, detailed error handling, and structured output generation. Coverage dedicated to evaluation, testing, and production deployment reflect the maturing LLM application landscape. Design secure, compliant AI systems with built-in production safeguards, responsible development practices, and a perspective on future research directions.The enhanced RAG coverage features techniques like hybrid search, re-rankers, and fact-checking mechanisms.Whether upgrading existing LLM applications or building new enterprise-scale solutions, by the end of the book, you will have updated knowledge on the practical patterns needed for production successWhat you will learnDesign and implement refined multi-agent systems using LangGraphEnterprise-grade testing and evaluation frameworks for LLM applicationsDeploy production-ready observability and monitoring solutionsBuild RAG systems with hybrid search and re-ranking capabilitiesImplement agents for software development and data analysisWork with latest LLMs and providers Google Gemini, Anthropic and Mistral, DeepSeek, and OpenAI o3-miniOptimize cost and performance across different deployment typesDesign secure, compliant AI systems with current best practicesWho this book is forThe book is for developers, researchers, and anyone interested in learning more about LangChain and LangGraph, wanting to build production-ready LLM applications. This book emphasizes on enterprise deployment patterns, making it especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this version also caters to engineering teams and decision-makers implementing enterprise-wide LLM strategies. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.Table of ContentsIntroduction to Large Language ModelsDevelopment Environment and SetupBuilding Workflows with LangChainRetrieval Augmented GenerationBuilding Intelligent AgentsAdvanced Applications and Multi-Agent SystemsSoftware Development and Data Analysis AgentsEvaluation and TestingObservability and Production DeploymentFuture Directions and Advanced Topics Read more

The Perfect User Guide for iPhone 11, iPhone 11 Pro and iPhone 11 Pro Max users that want to maximize the full features of the latest iOS 14 software. iOS 14 is one of Apple’s most significant iOS update to date, introducing Siri improvements, Picture in Picture video playback, Home screen design changes, App Library and several other powerful tweaks.This user manual will show you all the features and settings of the iOS 14 to help you master how to use the iOS 14 on your iPhone 11 with all its newly introduced feature. This guide will walk you through all the fantastic features of the new software and expose all the hidden setting. In this Guide you will learnWhat is new on iOS 14All the Amazing Features of the iOS 14 you should know aboutHow to Set Up Your iPhoneHow to Set up and manage Family SharingSend and Receive Money on iMessageDownload iMessage appsUpdating your iPhone device to the latest iOS 14Applying iOS 14 updates to your iPhone deviceUsing SiriUsing Find MyHow to add and manage Widgets to your iPhoneExploring the App Library on iOS 14How to use app clips on iPhoneUsing the Translate app on your iPhoneMultitasking with the Picture in Picture feature on your iPhoneHow to use the new Message features on your iPhoneSpeech and Language TranslationCreating your Memoji in iOS 14Using Memoji as a profile pictureCamera feature on iOS 14Sorting albumsFaceTime feature on iOS 14Headphone and Environmental sound control.How to set up sleep schedules in the Health app on iPhoneHow to set up and use Face Recognition on your iPhoneOrganizing apps with the App LibraryScanning QR codesAnd a lot more!Are you ready to get started with the new iOS 14? Then grab a copy of this manual Now! Read more