***Selected as CES 2018 Top Technology Book of the Year ***"Artificial intelligence" is the buzz word of the day. You've no doubt read your fair share of media hype either proclaiming doom and gloom where robots seize our jobs or prophesying a new utopia where AI cures all our human problems. But what does it actually mean for your role as a business leader? Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. If you want to drive innovation by combining data, technology, design, and people to solve real problems at an enterprise scale, this is your playbook. This book does not overload you with details on debugging TensorFlow code nor bore you with generalizations about the future of humanity. Instead, we teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments, and consciously designing your solutions to benefit both your organization and society as a whole. This book is focused on helping you drive concrete business decisions through applications of artificial intelligence and machine learning. Written with the combined knowledge of three experts in the field, Applied Artificial Intelligence is the best practical guide for business leaders looking to get true value from the adoption of machine learning technology. If you have questions such as...\nHow do I distinguish true value from AI hype?\nWhat are the best business use cases for AI established so far?\nHow do I identify the best business case for AI adoption and evaluate opportunities?\nShould I build or buy an AI platform?\nHow do I find and recruit top AI talent for my enterprise?\nHow will bringing AI into my business increase revenue or decrease costs?\nHow can I facilitate AI adoption within my company?...then this handbook provides you with answers.

Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success. Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life. Google, Amazon, Facebook, and similar tech giants are far from the only organizations on which artificial intelligence has had―and will continue to have―an incredibly significant result. AI is the present and the future of your business as well as your home life. Strengthening your prowess on the subject will prove invaluable to your preparation for the future of tech, and Artificial Intelligence Basics is the indispensable guide that you’ve been seeking.

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming—anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

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 SpaceXDeep 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.

Marvin Minsky -- one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT -- gives a revolutionary answer to the age-old question: "How does the mind work?" Minsky brilliantly portrays the mind as a "society" of tiny components that are themselves mindless. Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

Forget far-away dreams of the future. Artificial intelligence is here now!  Every time you use a smart device or some sort of slick technology--be it a smartwatch, smart speaker, security alarm, or even customer service chat box--you're engaging with artificial intelligence (AI). If you're curious about how AI is developed--or question whether AI is real--Artificial Intelligence For Dummies holds the answers you're looking for. Starting with a basic definition of AI and explanations of data use, algorithms, special hardware, and more, this reference simplifies this complex topic for anyone who wants to understand what operates the devices we can't live without.   This book will help you:  Separate the reality of artificial intelligence from the hype  Know what artificial intelligence can accomplish and what its limits are  Understand how AI speeds up data gathering and analysis to help you make informed decisions more quickly  See how AI is being used in hardware applications like drones, robots, and vehicles  Know where AI could be used in space, medicine, and communication fields sooner than you think  Almost 80 percent of the devices you interact with every day depend on some sort of AI. And although you don't need to understand AI to operate your smart speaker or interact with a bot, you'll feel a little smarter--dare we say more intelligent--when you know what's going on behind the scenes.  So don't wait. Pick up this popular guide to unlock the secrets of AI today! 

This book shows how federated machine learning allows multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private. Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example.In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Arthur C. Clarke has been the presiding genius of science fiction for almost fifty years. His works include the ground-breaking and profound CHILDHOOD'S END, RENDEZVOUS WITH RAMA and EXPEDITION TO EARTH. Written when landing on the moon was still a dream, made into one of the most influential films of our century, brilliant, compulsive, prophetic, 2001: A SPACE ODYSSEY tackles the enduring theme of man's place in the universe. On the moon an enigma is uncovered. So great are the implications that, for the first time, men are sent out deep into the solar system. But, before they can reach their destination, things begin to go wrong, horribly wrong...Look out for more information on this book and others on the Orbit website at www.orbitbooks.co.uk

Your no-nonsense guide to making sense of machine learningMachine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data&;or anything in between&;this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using AnacondaDive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Please Read Notes: Brand New, International Softcover Edition, Printed in black and white pages, minor self wear on the cover or pages, Sale restriction may be printed on the book, but Book name, contents, and author are exactly same as Hardcover Edition. Fast delivery through DHL/FedEx express.

♦♦Bonus: Buy the Paperback version of this book, and get the kindle eBook version included for FREE**\nMachine Learning is changing the world. You use Machine Learning every day and probably don’t know it. In this book, you will learn how ML grew from a desire to make computers able to learn. Trace the development of Machine Learning from the early days of a computer learning how to play checkers, to machines able to beat world masters in chess and go. Understand how large data is so important to Machine Learning, and how the collection of massive amounts of data provides Machine Learning programmers with the information they need to developing learning algorithms.Simple examples will help you understand the complex math and probability statistics underlining Machine Learning. You will also see real-world examples of Machine Learning in action and uncover how these algorithms are making your life better every day.Learn about how artificial intelligence, Machine Learning, Neural Networks, and Swarm Intelligence interact and complement each other as part of the quest to generate machines capable of thinking and reacting to the world. Read about the technical issues with Machine Learning and how they are being overcome. Discover the dark side of ML and what possible outcomes there could be should things go wrong. And finally, learn about the positive future artificial intelligence and Machine Learning promise to bring to the world.In this book, you will discover\n•The history of Machine Learning\n•Approaches taken to ML in the past and present\n•Artificial intelligence and its relationship to ML\n•How neural networks, big data, regression, and the cloud all play a part in the development of Machine Learning\n•Compare Machine Learning to the Internet of Things, Robotics, and Swarm Intelligence\n•Learn about the different models of ML and how each is used to produce learning algorithms\n•Get access to free software and data sets so you can try out your very own Machine Learning software\n•Examine some of the technical problems and philosophical dilemmas with ML\n•See what advanced Machine Learning will make to our world in the future\nSo what are you waiting for???Scroll back up and order this book NOW.

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021)Your comprehensive entry-level guide to machine learningWhile machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android―as in the movie Ex Machina―it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models―and way, way more.Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying―and fascinating―math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problemsWhether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Develop real-world applications powered by the latest advances in intelligent systems Key Features \nGain real-world contextualization using deep learning problems concerning research and application Get to know the best practices to improve and optimize your machine learning systems and algorithms Design and implement machine intelligence using real-world AI-based examples Book Description This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries. Throughout the Learning Path, you'll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way. By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects. This Learning Path includes content from the following Packt products: \nArtificial Intelligence By Example by Denis Rothman Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit What you will learn \nUse adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Understand future AI solutions and adapt quickly to them Master deep neural network implementation using TensorFlow Predict continuous target outcomes using regression analysis Dive deep into textual and social media data using sentiment analysis Who this book is for This Learning Path is for anyone who wants to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. You will learn to extend your machine learning and deep learning knowledge by creating practical AI smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this Learning Path. Table of Contents \nBecome an Adaptive Thinker Think Like a Machine Apply Machine Thinking to a Human Problem Become an Unconventional Innovator Manage the Power of Machine Learning and Deep Learning Focus on Optimizing Your Solutions When and How to Use Artificial Intelligence Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies Getting Your Neurons to Work Applying Biomimicking to Artificial Intelligence Conceptual Representation Learning Optimizing Blockchains with AI Cognitive NLP Chatbots Improve the Emotional Intelligence Deficiencies of Chatbots Building Deep Learning Environments Training NN for Prediction Using Regression Generative Language Model for Content Creation Building Speech Recognition with DeepSpeech2 Handwritten Digits Classification Using ConvNets Object Detection Using OpenCV and TensorFlow Building Face Recognition Using FaceNet Generative Adversarial Networks From GPUs to Quantum computing - AI Hardware TensorFlow Serving\n

"A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications"--

2020 Foreword Indie Award Honorable Mention in the "Science & Technology" Category Late in 2017, the global significance of the conversation about artificial intelligence (AI) changed forever. China put the world on alert when it released a plan to dominate all aspects of AI across the planet. Only weeks later, Vladimir Putin raised a Russian red flag in response by declaring AI the future for all humankind, and proclaiming that, "Whoever becomes the leader in this sphere will become the ruler of the world." The race was on. Consistent with their unique national agendas, countries throughout the world began plotting their paths and hurrying their pace. Now, not long after, the race has become a sprint. Despite everything at stake, to most of us AI remains shrouded by a cloud of mystery and misunderstanding. Hidden behind complicated and technical jargon and confused by fantastical depictions of science fiction, the modern realities of AI and its profound implications are hard to decipher, but crucial to recognize. In T-Minus AI: Humanity's Countdown to Artificial Intelligence and the New Pursuit of Global Power, author Michael Kanaan explains AI from a human-oriented perspective we can all finally understand. A recognized national expert and the U.S. Air Force's first Chairperson for Artificial Intelligence, Kanaan weaves a compelling new view on our history of innovation and technology to masterfully explain what each of us should know about modern computing, AI, and machine learning. Kanaan also dives into the global implications of AI by illuminating the cultural and national vulnerabilities already exposed and the pressing issues now squarely on the table. AI has already become China's all-purpose tool to impose its authoritarian influence around the world. Russia, playing catch up, is weaponizing AI through its military systems and now infamous, aggressive efforts to disrupt democracy by whatever disinformation means possible. America and like-minded nations are awakening to these new realities--and the paths they're electing to follow echo loudly the political foundations and, in most cases, the moral imperatives upon which they were formed. As we march toward a future far different than ever imagined, T-Minus AI is fascinating and crucially well-timed. It leaves the fiction behind, paints the alarming implications of AI for what they actually are, and calls for unified action to protect fundamental human rights and dignities for all.

A concise overview of machine learning―computer programs that learn from data―which underlies applications that include recommendation systems, face recognition, and driverless cars.\nToday, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition―as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning―the foundation of efforts to process that data into knowledge―has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.\nAlpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features \nMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: \nMastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn \nExplore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models\n Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path. Table of Contents \nMachine Learning Model Fundamentals Introduction to Semi-Supervised Learning Graph-Based Semi-Supervised Learning Bayesian Networks and Hidden Markov Models EM Algorithm and Applications Hebbian Learning and Self-Organizing Maps Clustering Algorithms Advanced Neural Models Classical Machine Learning with TensorFlow Neural Networks and MLP with TensorFlow and Keras RNN with TensorFlow and Keras CNN with TensorFlow and Keras Autoencoder with TensorFlow and Keras TensorFlow Models in Production with TF Serving Deep Reinforcement Learning Generative Adversarial Networks Distributed Models with TensorFlow Clusters Debugging TensorFlow Models Tensor Processing Units Getting Started Image Classification Image Retrieval Object Detection Semantic Segmentation Similarity Learning\n

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."Everything you really need to know in Machine Learning in a hundred pages.

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our ownIn the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world\nNo recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.\nIn Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent―really―are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. \nInterweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

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'if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: \nhow intelligence can be defined whether machines can 'think' sensory input in machine systems the nature of consciousness the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.

Please note the second edition of this title with updated content is now available on Amazon. Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey into the world of machine learning, there is basic theory to march through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book offers a practical and high-level introduction to machine learning. Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land. In this step-by-step guide you will learn:- The very basics of Machine Learning that all beginners need to master - Association Analysis used in the retail and E-commerce space - Recommender Systems as you've seen online, including Amazon - Decision Trees for visually mapping and classifying decision processes - Regression Analysis to create trend lines and predict trends - Data Reduction and Principle Component Analysis to cut through the noise - k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings - Introduction to Deep Learning/Neural Networks - Bias/Variance to optimize your machine learning model - How to build your first machine learning model to predict video game sales using Python - Careers in the field Please also note that under Amazon’s Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost.

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 algorithms Conduct research with ML algorithms on big data Use 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.

A New York Times bestsellerSuperintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.

Financial Times Best Books of the Year 2018TechRepublic Top Books Every Techie Should ReadBook DescriptionHow 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.

NEW YORK TIMES BESTSELLER • How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing humans on the job market and perhaps altogether? Will AI help life flourish like never before or give us more power than we can handle?What sort of future do you want? This book empowers you to join what may be the most important conversation of our time. It doesn’t shy away from the full range of viewpoints or from the most controversial issues—from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.

The future is now. Acclaimed technologist and inventor Amir Husain explains how we can live amidst the coming age of sentient machines and artificial intelligence—and not only survive, but thrive.Artificial “machine” intelligence is playing an ever-greater role in our society. We are already using cruise control in our cars, automatic checkout at the drugstore, and are unable to live without our smartphones. The discussion around AI is polarized; people think either machines will solve all problems for everyone, or they will lead us down a dark, dystopian path into total human irrelevance. Regardless of what you believe, the idea that we might bring forth intelligent creation can be intrinsically frightening. But what if our greatest role as humans so far is that of creators?Amir Husain, a brilliant inventor and computer scientist, argues that we are on the cusp of writing our next, and greatest, creation myth. It is the dawn of a new form of intellectual diversity, one that we need to embrace in order to advance the state of the art in many critical fields, including security, resource management, finance, and energy. “In The Sentient Machine, Husain prepares us for a brighter future; not with hyperbole about right and wrong, but with serious arguments about risk and potential” (Dr. Greg Hyslop, Chief Technology Officer, The Boeing Company). He addresses broad existential questions surrounding the coming of AI: Why are we valuable? What can we create in this world? How are we intelligent? What constitutes progress for us? And how might we fail to progress? Husain boils down complex computer science and AI concepts into clear, plainspoken language and draws from a wide variety of cultural and historical references to illustrate his points. Ultimately, Husain challenges many of our societal norms and upends assumptions we hold about “the good life.”

In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking. By examining these different forms of mind activity, Minsky says, we can explain why our thought sometimes takes the form of carefully reasoned analysis and at other times turns to emotion. He shows how our minds progress from simple, instinctive kinds of thought to more complex forms, such as consciousness or self-awareness. And he argues that because we tend to see our thinking as fragmented, we fail to appreciate what powerful thinkers we really are. Indeed, says Minsky, if thinking can be understood as the step-by-step process that it is, then we can build machines - artificial intelligences - that not only can assist with our thinking by thinking as we do but have the potential to be as conscious as we are. Eloquently written, The Emotion Machine is an intriguing look into a future where more powerful artificial intelligences await.

"The most important book on AI this year." --The Guardian"Mr. Russell's exciting book goes deep, while sparkling with dry witticisms." --The Wall Street Journal"The most important book I have read in quite some time" (Daniel Kahneman); "A must-read" (Max Tegmark); "The book we've all been waiting for" (Sam Harris)A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machinesIn the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage.If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial.