Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Leading agile consultant and practitioner Mike Cohn presents detailed recommendations, powerful tips, and real-world case studies drawn from his unparalleled experience helping hundreds of software organizations make Scrum and agile work. "Succeeding with Agile" is for pragmatic software professionals who want real answers to the most difficult challenges they face in implementing Scrum. Cohn covers every facet of the transition: getting started, helping individuals transition to new roles, structuring teams, scaling up, working with a distributed team, and finally, implementing effective metrics and continuous improvement.Throughout, Cohn presents “Things to Try Now” sections based on his most successful advice. Complementary “Objection” sections reproduce typical conversations with those resisting change and offer practical guidance for addressing their concerns. Coverage includes: - Practical ways to get started immediately–and “get good” fast - Overcoming individual resistance to the changes Scrum requires - Staffing Scrum projects and building effective teams - Establishing “improvement communities” of people who are passionate about driving change - Choosing which agile technical practices to use or experiment with - Leading self-organizing teams - Making the most of Scrum sprints, planning, and quality techniques - Scaling Scrum to distributed, multiteam projects - Using Scrum on projects with complex sequential processes or challenging compliance and governance requirements - Understanding Scrum’s impact on HR, facilities, and project managementWhether you've completed a few sprints or multiple agile projects and whatever your role–manager, developer, coach, ScrumMaster, product owner, analyst, team lead, or project lead–this book will help you succeed with your very next project. Then, it will help you go much further: It will help you transform your entire development organization.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    But if one per year is good, how about Seven Languages in Seven Weeks? In this book you'll get a hands-on tour of Clojure, Haskell, Io, Prolog, Scala, Erlang, and Ruby. Whether or not your favorite language is on that list, you'll broaden your perspective of programming by examining these languages side-by-side. You'll learn something new from each, and best of all, you'll learn how to learn a language quickly. Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them. Among this group of seven, you'll explore the most critical programming models of our time. Learn the dynamic typing that makes Ruby, Python, and Perl so flexible and compelling. Understand the underlying prototype system that's at the heart of JavaScript. See how pattern matching in Prolog shaped the development of Scala and Erlang. Discover how pure functional programming in Haskell is different from the Lisp family of languages, including Clojure. Explore the concurrency techniques that are quickly becoming the backbone of a new generation of Internet applications. Find out how to use Erlang's let-it-crash philosophy for building fault-tolerant systems. Understand the actor model that drives concurrency design in Io and Scala. Learn how Clojure uses versioning to solve some of the most difficult concurrency problems. It's all here, all in one place. Use the concepts from one language to find creative solutions in another-or discover a language that may become one of your favorites.

    In that spirit, Edge.org publisher and author of Know This, John Brockman, asks 206 of the world’s most brilliant minds the 2017 Edge Question: What scientific term or concept ought to be more widely known?Contributors include: author of The God Delusion RICHARD DAWKINS on using animals’ “Genetic Book of the Dead” to reconstruct ecological history; MacArthur Fellow REBECCA NEWBERGER GOLDSTEIN on “scientific realism,” the idea that scientific theories explain phenomena beyond what we can see and touch; author of Seven Brief Lessons on Physics CARLO ROVELLI on “relative information,” which governs the physical world around us; theoretical physicist LAWRENCE M. KRAUSS on the hidden blessings of “uncertainty”; cognitive scientist and author of The Language Instinct STEVEN PINKER on “The Second Law of Thermodynamics”; biogerontologist AUBREY DE GREY on why “maladaptive traits” have been conserved evolutionarily; musician BRIAN ENO on “confirmation bias” in the internet age; Man Booker-winning author of Atonement IAN MCEWAN on the “Navier-Stokes Equations,” which govern everything from weather prediction to aircraft design and blood flow; plus pieces from RICHARD THALER, JARED DIAMOND, NICHOLAS CARR, JANNA LEVIN, LISA RANDALL, KEVIN KELLY, DANIEL COLEMAN, FRANK WILCZEK, RORY SUTHERLAND, NINA JABLONSKI, MARTIN REES, ALISON GOPNIK, and many, many others.

    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?

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    For centuries, the power of zero savored of the demonic; once harnessed, it became the most important tool in mathematics. Zero follows this number from its birth as an Eastern philosophical concept to its struggle for acceptance in Europe and its apotheosis as the mystery of the black hole. Today, zero lies at the heart of one of the biggest scientific controversies of all time, the quest for the theory of everything. Elegant, witty, and enlightening, Zero is a compelling look at the strangest number in the universe and one of the greatest paradoxes of human thought.

    Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

     -Byte, September 1995 I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me! I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up. -Charles Long If you think you're a really good programmer... read [Knuth's] Art of Computer Programming... You should definitely send me a resume if you can read the whole thing. -Bill Gates It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers. -Jonathan Laventhol This first volume in the series begins with basic programming concepts and techniques, then focuses more particularly on information structures-the representation of information inside a computer, the structural relationships between data elements and how to deal with them efficiently. Elementary applications are given to simulation, numerical methods, symbolic computing, software and system design. Dozens of simple and important algorithms and techniques have been added to those of the previous edition. The section on mathematical preliminaries has been extensively revised to match present trends in research. Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP), http: //msp.org

    And how to move all of this data becomes nearly as important as the data itself. If you� re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you� ll learn Kafka� s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem.Explore Kafka producers and consumers for writing and reading messagesUnderstand Kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasksLearn the most critical metrics among Kafka� s operational measurementsExplore how Kafka� s stream delivery capabilities make it a perfect source for stream processing systems

    From the transistor to the laser, it s hard to find an aspect of modern life that hasn t been touched by Bell Labs. Why did so many transformative ideas come from Bell Labs? In "The Idea Factory," Jon Gertner traces the origins of some of the twentieth century s most important inventions and delivers a riveting and heretofore untold chapter of American history. At its heart this is a story about the life and work of a small group of brilliant and eccentric men Mervin Kelly, Bill Shockley, Claude Shannon, John Pierce, and Bill Baker who spent their careers at Bell Labs. Their job was to research and develop the future of communications. Small-town boys, childhood hobbyists, oddballs: they give the lie to the idea that Bell Labs was a grim cathedral of top-down command and control.Gertner brings to life the powerful alchemy of the forces at work behind Bell Labs inventions, teasing out the intersections between science, business, and society. He distills the lessons that abide: how to recruit and nurture young talent; how to organize and lead fractious employees; how to find solutions to the most stubbornly vexing problems; how to transform a scientific discovery into a marketable product, then make it even better, cheaper, or both. Today, when the drive to invent has become a mantra, Bell Labs offers us a way to enrich our understanding of the challenges and solutions to technological innovation. Here, after all, was where the foundational ideas on the management of innovation were born. "The Idea Factory" is the story of the origins of modern communications and the beginnings of the information age a deeply human story of extraordinary men who were given extraordinary means time, space, funds, and access to one another and edged the world into a new dimension."