The Master Algorithm

The Master Algorithm Author Pedro Domingos
ISBN-10 9780241004555
Year 2015-09-22
Pages 352
Language en
Publisher Penguin UK
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A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.

The Master Algorithm

The Master Algorithm Author Pedro Domingos
ISBN-10 9780465061921
Year 2015-09-22
Pages 352
Language en
Publisher Hachette UK
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In 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.

The Master Algorithm

The Master Algorithm Author Pedro Domingos
ISBN-10 9780465061921
Year 2015-09-22
Pages 352
Language en
Publisher Basic Books
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In 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.

Mastering Algorithms with C

Mastering Algorithms with C Author Kyle Loudon
ISBN-10 0596551959
Year 1999-08-05
Pages 562
Language en
Publisher "O'Reilly Media, Inc."
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There are many books on data structures and algorithms, including some with useful libraries of C functions. Mastering Algorithms with C offers you a unique combination of theoretical background and working code. With robust solutions for everyday programming tasks, this book avoids the abstract style of most classic data structures and algorithms texts, but still provides all of the information you need to understand the purpose and use of common programming techniques. Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included. Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs. He explains how to use algorithms for sorting, searching, numerical analysis, data compression, data encryption, common graph problems, and computational geometry. And he describes the relative efficiency of all implementations. The compression and encryption chapters not only give you working code for reasonably efficient solutions, they offer explanations of concepts in an approachable manner for people who never have had the time or expertise to study them in depth. Anyone with a basic understanding of the C language can use this book. In order to provide maintainable and extendible code, an extra level of abstraction (such as pointers to functions) is used in examples where appropriate. Understanding that these techniques may be unfamiliar to some programmers, Loudon explains them clearly in the introductory chapters. Contents include: Pointers Recursion Analysis of algorithms Data structures (lists, stacks, queues, sets, hash tables, trees, heaps, priority queues, graphs) Sorting and searching Numerical methods Data compression Data encryption Graph algorithms Geometric algorithms

How to Think About Algorithms

How to Think About Algorithms Author Jeff Edmonds
ISBN-10 9781139471756
Year 2008-05-19
Pages
Language en
Publisher Cambridge University Press
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This textbook, for second- or third-year students of computer science, presents insights, notations, and analogies to help them describe and think about algorithms like an expert, without grinding through lots of formal proof. Solutions to many problems are provided to let students check their progress, while class-tested PowerPoint slides are on the web for anyone running the course. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author guides students around the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. The book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a careful and clear way, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems.

Superintelligence

Superintelligence Author Nick Bostrom
ISBN-10 9780199678112
Year 2014
Pages 328
Language en
Publisher Oxford University Press (UK)
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Human beings occupy a dominant position on our planet, not because we have stronger muscles or sharper teeth than other species, but because we have smarter brains. Our brains developed the technologies and the complex social organization that make us powerful. For example, our smartness gave us bulldozers and knives that are stronger and sharper than any animal's muscles or teeth.If machine brains come to surpass human brains as ours surpass those of other animals, the machine brains could become as powerful relative to us as we are to the other animals. Extreme levels of machine intelligence - superintelligence - would potentially be in a position to shape the future. What happens to humanity, whether humanity would even survive, would then depend on the goals of the superintelligence. The possibility of a machine intelligence revolution is therefore an extremely important topic. Perhaps it is the most important topic.Readership : Suitable for general readers as well as academics in the fields of Artificial Intelligence and Machine Learning, Computer Science, and Philosophy.

Python Algorithms

Python Algorithms Author Magnus Lie Hetland
ISBN-10 9781484200551
Year 2014-09-17
Pages 320
Language en
Publisher Apress
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Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.

Data Algorithms

Data Algorithms Author Mahmoud Parsian
ISBN-10 9781491906156
Year 2015-07-13
Pages 778
Language en
Publisher "O'Reilly Media, Inc."
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If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

Understanding Machine Learning

Understanding Machine Learning Author Shai Shalev-Shwartz
ISBN-10 9781107057135
Year 2014-05-19
Pages 409
Language en
Publisher Cambridge University Press
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Machine Learning

Machine Learning Author Ethem Alpaydin
ISBN-10 9780262529518
Year 2016-10-07
Pages 224
Language en
Publisher MIT Press
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A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.

Probably Approximately Correct

Probably Approximately Correct Author Leslie Valiant
ISBN-10 9780465037902
Year 2013-06-04
Pages 208
Language en
Publisher Hachette UK
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We have effective theories for very few things. Gravity is one, electromagnetism another. But for most things—whether as mundane as finding a mate or as major as managing an economy—our theories are lousy or nonexistent. Fortunately, we don't need them, any more than a fish needs a theory of water to swim; we're able to muddle through. But how do we do it? In Probably Approximately Correct, computer scientist Leslie Valiant presents a theory of the theoryless. The key is “probably approximately correct” learning, Valiant's model of how anything can act without needing to understand what is going on. The study of probably approximately correct algorithms reveals the shared computational nature of evolution and cognition, indicates how computers might possess authentic intelligence, and shows why hacking a problem can be far more effective than developing a theory to explain it. After all, finding a mate is a lot more satisfying than finding a theory of mating. Offering an elegant, powerful model that encompasses all of life's complexity, Probably Approximately Correct will revolutionize the way we look at the universe's greatest mysteries.

Algorithms to Live By

Algorithms to Live By Author Brian Christian
ISBN-10 9781627790376
Year 2016-04-19
Pages 368
Language en
Publisher Henry Holt and Company
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A fascinating exploration of how computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same constraints, so computer scientists have been grappling with their version of such problems for decades. And the solutions they've found have much to teach us. In a dazzlingly interdisciplinary work, acclaimed author Brian Christian (who holds degrees in computer science, philosophy, and poetry, and works at the intersection of all three) and Tom Griffiths (a UC Berkeley professor of cognitive science and psychology) show how the simple, precise algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of human memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

The Intelligent Web

The Intelligent Web Author Gautam Shroff
ISBN-10 9780199646715
Year 2013-11
Pages 295
Language en
Publisher Oxford University Press
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Early hopes for Artificial Intelligence soon evaporated. But, driven by the need for smarter searching and advert placing, increasingly sophisticated algorithms, combined with the sheer amount of data on the Web, have led to a growing "Web intelligence". Gautam Shroff explores this trend, its conceptual basis, and what the future may hold.

Python Machine Learning

Python Machine Learning Author Sebastian Raschka
ISBN-10 9781783555147
Year 2015-09-23
Pages 454
Language en
Publisher Packt Publishing Ltd
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Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Weapons of Math Destruction

Weapons of Math Destruction Author Cathy O'Neil
ISBN-10 9780553418828
Year 2016-09-06
Pages 272
Language en
Publisher Crown
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Longlisted for the National Book Award New York Times Bestseller A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to rip apart our social fabric We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data. Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change. — Longlist for National Book Award (Non-Fiction) — Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology) — Kirkus, Best Books of 2016 — New York Times, 100 Notable Books of 2016 (Non-Fiction) — The Guardian, Best Books of 2016 — WBUR's "On Point," Best Books of 2016: Staff Picks — Boston Globe, Best Books of 2016, Non-Fiction