Download or read book DeepMind A Technical Deep Dive written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-06-18 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystifying DeepMind's AI: A Journey Through Cutting-Edge Technologies and Their Impact Dive into the fascinating world of DeepMind, a pioneering artificial intelligence (AI) research laboratory at the forefront of scientific discovery. This comprehensive book unveils the mysteries behind DeepMind's groundbreaking achievements, exploring the core technologies that power their intelligent machines. Unveiling the Magic: Deep Learning, Reinforcement Learning, and More Embark on a learning adventure as you gain a clear understanding of DeepMind's core AI technologies. Discover the power of Deep Learning, a method inspired by the human brain's structure and function, allowing AI to learn complex patterns from vast amounts of data. Go deeper into Reinforcement Learning, where AI agents learn through trial and error, interacting with an environment and receiving rewards for desired actions. Explore how DeepMind integrates Probabilistic Modeling to reason under uncertainty, a crucial capability for real-world applications. Beyond Games: Conquering Complex Challenges Witness the remarkable success of DeepMind's AI in conquering complex games like Go, StarCraft II, and beyond. Learn how these achievements showcase the ability of AI to handle complex decision-making and strategic planning in dynamic environments. But DeepMind's reach extends far beyond games. Delve into their contributions to scientific breakthroughs, including protein folding, a critical process in biology with vast implications for drug discovery and disease research. Shaping a Sustainable Future with AI Explore DeepMind's commitment to tackling real-world challenges using AI for positive social impact. Discover their research in harnessing AI for weather forecasting, optimizing energy consumption in data centers, and supporting the growth of renewable energy sources. See how DeepMind's AI is helping us combat climate change and build a more sustainable future. The Ethical Landscape: Ensuring Safe and Explainable AI As AI becomes more powerful, ethical considerations become paramount. This book explores DeepMind's approach to developing safe and explainable AI. Learn about their strategies for mitigating risks, ensuring human oversight, and incorporating ethical considerations into AI design. Understand the importance of explainable AI models that allow humans to comprehend the decision-making process behind AI actions. The Future of Work and the Broader Societal Impact Explore the potential impact of DeepMind's AI on the future of work. While automation is a concern, this book delves into the potential for AI to augment human capabilities, increase efficiency, and create new job opportunities. Look beyond the workplace to discover how DeepMind's AI has the potential to revolutionize healthcare, education, and scientific discovery as a whole. A Call to Action: Shaping the Future of AI Together This book concludes by emphasizing the importance of collective action in shaping the future of AI. With increased public awareness, education, and responsible development practices, AI has the potential to create a better future for all. Join the conversation and learn how you can contribute to a future where AI benefits humanity. Throughout the book, complex concepts are explained in an engaging and understandable manner, making this a valuable resource for anyone interested in artificial intelligence, DeepMind's groundbreaking work, and the potential impact of AI on our world.

Download or read book A Deep Dive into Understanding How Google Works written by Adid Khan and published by Adid Khan. This book was released on 2024-04-24 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the exploration of 'How Google Works.' This ebook delves into the intricate workings of one of the most influential tech companies of our time, unraveling the layers of Google's success and its profound impact on various aspects of our lives. From the foundation of Google to its avant-garde initiatives in artificial intelligence, sustainability, and beyond, each chapter unveils a different facet of Google's diverse portfolio. Join us on this journey as we navigate through the history, innovation, and future prospects of a company that has redefined the digital landscape. In a world where Google is an omnipresent force, understanding 'How Google Works' becomes imperative. This ebook serves as a comprehensive guide to decipher the inner workings of Google, from its humble beginnings to its global dominance. Through a series of chapters, we peel back the curtain on Google's algorithms, culture, impact on society and the environment, ethical dilemmas, and groundbreaking initiatives. Whether you're a tech enthusiast, a business professional, or simply curious about the tech giant that shapes our online experiences, this book offers a deep dive into the ethos and evolution of Google.

Download or read book Deep Diving into Data Protection written by Jean Herveg and published by Éditions Larcier. This book was released on 2022-03-24 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book celebrates the 40th anniversary of the creation of the CRID and the 10th anniversary of its successor, the CRIDS. It gathers twenty-one very high quality contributions on extremely interesting and topical aspects of data protection. The authors come from Europe as well as from the United States of America and Canada. Their contributions have been grouped as follows: 1° ICT Governance; 2° Commodification & Competition; 3° Secret surveillance; 4° Whistleblowing; 5° Social Medias, Web Archiving & Journalism; 6° Automated individual decision-making; 7° Data Security; 8° Privacy by design; 9° Health, AI, Scientific Research & Post-Mortem Privacy. This book is intended for all academics, researchers, students and practitioners who have an interest in privacy and data protection.

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Download or read book The Coming Wave written by Mustafa Suleyman and published by Crown. This book was released on 2023-09-05 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: NEW YORK TIMES BESTSELLER • An urgent warning of the unprecedented risks that AI and other fast-developing technologies pose to global order, and how we might contain them while we have the chance—from a co-founder of the pioneering artificial intelligence company DeepMind “A fascinating, well-written, and important book.”—Yuval Noah Harari “Essential reading.”—Daniel Kahneman “An excellent guide for navigating unprecedented times.”—Bill Gates A Best Book of the Year: Economist, Financial Times, CEO Magazine • Winner of the Inc. Non-Obvious Book Award • Finalist for the Porchlight Business Book Award and the Financial Times and Schroders Business Book of the Year Award We are approaching a critical threshold in the history of our species. Everything is about to change. Soon you will live surrounded by AIs. They will organise your life, operate your business, and run core government services. You will live in a world of DNA printers and quantum computers, engineered pathogens and autonomous weapons, robot assistants and abundant energy. None of us are prepared. As co-founder of the pioneering AI company DeepMind, part of Google, Mustafa Suleyman has been at the centre of this revolution. The coming decade, he argues, will be defined by this wave of powerful, fast-proliferating new technologies. In The Coming Wave, Suleyman shows how these forces will create immense prosperity but also threaten the nation-state, the foundation of global order. As our fragile governments sleepwalk into disaster, we face an existential dilemma: unprecedented harms on one side, the threat of overbearing surveillance on the other. Can we forge a narrow path between catastrophe and dystopia? This groundbreaking book from the ultimate AI insider establishes “the containment problem”—the task of maintaining control over powerful technologies—as the essential challenge of our age.

Download or read book Decision Intelligence written by Miriam O'Callaghan and published by CRC Press. This book was released on 2023-04-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Provides a multidisciplinary approach of building knowledge on DI; 2. Discusses the limits of the human brain and why computer models are better at making decisions; 3. Covers agent programs for AI-powered decision-making agents; 4. Presents a DI framework - flowchart and figures; 5. Includes detailed and comprehensive information on DI tools and technologies; 6. Gives an ethics-focused approach to building DI systems for the protection of human rights and wellbeing.

Download or read book Deep Reinforcement Learning written by Mohit Sewak and published by Springer. This book was released on 2019-06-27 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.

Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Download or read book Machine Learning with PyTorch and Scikit Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Download or read book The Market Mind Hypothesis written by Patrick Schotanus and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-10-04 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is economics’ missing link? Recent economic crises have had a devastating impact on society. Worryingly, they gravely risked a collapse of the financial system. These crises also painfully revealed economics’ blind spots. Crucially, economics is not an innocent bystander but central to the problem. In this pioneering book, Patrick Schotanus explains that economics’ mechanical worldview is the ontological error which leads to flawed thinking and faulty practices. The Market Mind Hypothesis (MMH) thus calls it "mechanical economics": it not only erroneously views but also dangerously treats the economy as a machine, the market as an automaton, and its agents as robots. Inspired by heterodox economic and leading cognitive thinkers, this book offers an alternative paradigm. Central to MMH’s psychophysical worldview is the fact that consumers, investors, and other participants are conscious beings and that their minds’ extension makes consciousness a reality in markets, exemplified by market mood. Specifically, denial of the complex mind~matter exchanges as the essence of markets means the extended mind~body problem is economics’ elephant in the room. The book argues that if mechanical economics is the answer, we have been asking the wrong questions. Moreover, we will not solve our economic predicaments by doubling down on the assumption of rationality, nor by identifying yet another behavioural bias. Instead, scholars and students of economics and finance as well as finance practitioners need to investigate—through cognitive economics—the deep links between markets and minds to better understand both. With a foreword by investment strategist Russell Napier, an intermezzo by neuroscientist and complexity pioneer Scott Kelso, and an afterword by 4E cognition philosopher Julian Kiverstein.

Download or read book Grokking Deep Reinforcement Learning written by Miguel Morales and published by Manning Publications. This book was released on 2020-11-10 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. Summary We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. What's inside An introduction to reinforcement learning DRL agents with human-like behaviors Applying DRL to complex situations About the reader For developers with basic deep learning experience. About the author Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course. Table of Contents 1 Introduction to deep reinforcement learning 2 Mathematical foundations of reinforcement learning 3 Balancing immediate and long-term goals 4 Balancing the gathering and use of information 5 Evaluating agents’ behaviors 6 Improving agents’ behaviors 7 Achieving goals more effectively and efficiently 8 Introduction to value-based deep reinforcement learning 9 More stable value-based methods 10 Sample-efficient value-based methods 11 Policy-gradient and actor-critic methods 12 Advanced actor-critic methods 13 Toward artificial general intelligence

Download or read book Embodiment and the Inner Life written by Murray Shanahan and published by . This book was released on 2010 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: To understand the mind and its place in Nature is one of the great intellectual challenges of our time, a challenge that is both scientific and philosophical. How does cognition influence an animal's behaviour? What are its neural underpinnings? How is the inner life of a human being constituted? What are the neural underpinnings of the conscious condition? Embodiment and the Inner Life approaches each of these questions from a scientific standpoint. But it contends that, before we can make progress on them, we have to give up the habit of thinking metaphysically, a habit that creates a fog of philosophical confusion. From this post-reflective point of view, the book argues for an intimate relationship between cognition, sensorimotor embodiment, and the integrative character of the conscious condition. Drawing on insights from psychology, neuroscience, and dynamical systems, it proposes an empirical theory of this three-way relationship whose principles, not being tied to the contingencies of biology or physics, are applicable to the whole space of possible minds in which humans and other animals are included. Embodiment and the Inner Life is one of very few books that provides a properly joined-up theory of consciousness, and will be essential reading for all psychologists, philosophers, and neuroscientists with an interest in the enduring puzzle of consciousness.

Download or read book Artificial Intelligence and the Future of Defense written by Stephan De Spiegeleire and published by The Hague Centre for Strategic Studies. This book was released on 2017-05-17 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is on everybody’s minds these days. Most of the world’s leading companies are making massive investments in it. Governments are scrambling to catch up. Every single one of us who uses Google Search or any of the new digital assistants on our smartphones has witnessed first-hand how quickly these developments now go. Many analysts foresee truly disruptive changes in education, employment, health, knowledge generation, mobility, etc. But what will AI mean for defense and security? In a new study HCSS offers a unique perspective on this question. Most studies to date quickly jump from AI to autonomous (mostly weapon) systems. They anticipate future armed forces that mostly resemble today’s armed forces, engaging in fairly similar types of activities with a still primarily industrial-kinetic capability bundle that would increasingly be AI-augmented. The authors of this study argue that AI may have a far more transformational impact on defense and security whereby new incarnations of ‘armed force’ start doing different things in novel ways. The report sketches a much broader option space within which defense and security organizations (DSOs) may wish to invest in successive generations of AI technologies. It suggests that some of the most promising investment opportunities to start generating the sustainable security effects that our polities, societies and economies expect may lie in in the realms of prevention and resilience. Also in those areas any large-scale application of AI will have to result from a preliminary open-minded (on all sides) public debate on its legal, ethical and privacy implications. The authors submit, however, that such a debate would be more fruitful than the current heated discussions about ‘killer drones’ or robots. Finally, the study suggests that the advent of artificial super-intelligence (i.e. AI that is superior across the board to human intelligence), which many experts now put firmly within the longer-term planning horizons of our DSOs, presents us with unprecedented risks but also opportunities that we have to start to explore. The report contains an overview of the role that ‘intelligence’ - the computational part of the ability to achieve goals in the world - has played in defense and security throughout human history; a primer on AI (what it is, where it comes from and where it stands today - in both civilian and military contexts); a discussion of the broad option space for DSOs it opens up; 12 illustrative use cases across that option space; and a set of recommendations for - especially - small- and medium sized defense and security organizations.

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Download or read book Pro JavaFX 2 written by James Weaver and published by Apress. This book was released on 2012-06-12 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Pro JavaFX 2: A Definitive Guide to Rich Clients with Java Technology, Jim Weaver, Weiqi Gao, Stephen Chin, Dean Iverson, and Johan Vos show you how you can use the JavaFX platform to create rich-client Java applications. You'll see how JavaFX provides a powerful Java-based UI platform capable of handling large-scale data-driven business applications. Covering the JavaFX API, development tools, and best practices, this book provides code examples that explore the exciting new features provided with JavaFX 2. It contains engaging tutorials that cover virtually every facet of JavaFX development and reference materials on JavaFX that augment the JavaFX API documentation. Written in an engaging and friendly style, Pro JavaFX 2 is an essential guide to JavaFX 2.

Download or read book Architects of Intelligence written by Martin Ford and published by Packt Publishing Ltd. This book was released on 2018-11-23 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Times Best Books of the Year 2018 TechRepublic Top Books Every Techie Should Read Book Description How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel). Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future. Meet the minds behind the AI superpowers as they discuss the science, business and ethics of modern artificial intelligence. Read James Manyika’s thoughts on AI analytics, Geoffrey Hinton’s breakthroughs in AI programming and development, and Rana el Kaliouby’s insights into AI marketing. This AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and Yoshua Bengio (leader in deep learning) to complete your AI education and give you an AI advantage in 2019 and the future.

Download or read book Deep Learning at Scale written by Suneeta Mall and published by "O'Reilly Media, Inc.". This book was released on 2024-06-18 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale