However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    When John Hargrave first invested in cryptocurrency, the price of a single bitcoin was about $125; a few years later, that same bitcoin was worth $20,000. He wasn’t alone: this flood of new money is like the early days of the Internet, creating a new breed of “blockchain billionaires.” Sir John has unlocked their secrets. In Blockchain for Everyone, Sir John reveals the formula for investing in bitcoin and blockchain, using real-life stories, easy-to-understand examples, and a healthy helping of humor. Packed with illustrations, Blockchain for Everyone explains how (and when) to buy bitcoin, cryptocurrencies, and other blockchain assets, with step-by-step instructions. Blockchain for Everyone is the first blockchain investing book written for the layperson: a guide that helps everyone understand how to build wealth wisely. It’s the new investing manifesto!

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    It bridges the gap between the mechanics of econometrics and modern applications of econometrics by employing a systematic approach motivated by the major problems facing applied researchers today. Throughout the text, the emphasis on examples gives a concrete reality to economic relationships and allows treatment of interesting policy questions in a realistic and accessible framework.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    Find all of the following, explained in plain-English with no legal jargon:Business Taxation 101: A brief primer on tax topics in general, especially as they apply to businesses.Home Office Deduction: How to ensure you qualify for it and how to calculate it.Estimated Tax payments: When and how to pay them, as well as an easy way to calculate each payment.Self-Employment Tax: What it is, why it exists, and how to calculate it.Business Retirement Plans: What the different types are, and which one is best for you.Numerous Business Deductions: Several deductions explained in detail, including how to make sure you can qualify to take them and how to maximize them.Audit Protection: Learn what records you need to keep (and how long to keep them) in order to protect yourself in case of an audit.

    In addition to general advice on basic numeracy, the guide points out common errors and explains the recognized techniques for solving financial problems, analysing information of any kind, and effective decision making. Over one hundred charts, graphs, tables, and feature boxes highlight key points. Also included is an A-Z dictionary of terms covering everything from amortization to zero-sum game. Whatever your business, The Economist Numbers Guide will prove invaluable.

    That is why a typical investor loses more than one million dollars in his/her investing career (see pages 172-175). The GOOD news is that you can smile now because finally there is a simple solution. Robert Rolih will share with you the details and secret subtleties that will enable you to fire your financial adviser, take control of your money and investments and outperform the pros - while spending only a few hours per year. By reading The Million Dollar Decision, you'll finally discover how to make sure your invested money works for you - and not for the financial industry: The Six Dark Forces of Investing: if you don't learn what these forces are, you will never be able to invest profitably. Get to know them, and Darth Vader will seem like a good guy to you. The Commission Camouflage Effect: learn how the financial industry takes most of your future returns - without you even being aware of it. The most important formula of your financial life: Financial Success Formula. This is an entirely new philosophy on personal finance and investing that will, quite literally, save you financially. If you have adult children, you will want to share it with them right away. Financial safety with the A.R.M.O.R. Formula: even if you are the kindest and most positive person, something unforeseen can happen that turns everything around. Use this simple formula to prevent financial disasters from disrupting your financial security. Selecting the right financial products and minimizing risk: stocks, bonds, mutual funds, index funds, gold, silver. Get a clear answer on how to make the right choices. Investing for retirement or to grow your wealth simply can't get easier! How to beat Richard Branson's speed of creating wealth with one smart move? No hype. Just facts. This will come as a total surprise for you. Short-Term Investing Slaughterhouse: learn the sad truth about short-term trading. If you are thinking about trading stocks, Forex, binary options and other instruments, this chapter will be a life saver. How to get out of the rigged game of investing and add a million to your net worth in just a couple of hours per year? By reading this book you'll have total confidence in yourself when investing and outperform even the experts in just a couple of hours per year. Add this book to cart now and make your Million Dollar Decision! "Being a doctor of medicine I have never enjoyed books about personal finance and investing.

    As the recent collapse on Wall Street shows, we are often ill-equipped to deal with uncertainty and risk. Yet every day we base our personal and business plans on uncertainties, whether they be next month's sales, next year's costs, or tomorrow's stock price. In The Flaw of Averages, Sam Savage-known for his creative exposition of difficult subjects- describes common avoidable mistakes in assessing risk in the face of uncertainty. Along the way, he shows why plans based on average assumptions are wrong, on average, in areas as diverse as healthcare, accounting, the War on Terror, and climate change. In his chapter on Sex and the Central Limit Theorem, he bravely grasps the literary third rail of gender differences.Instead of statistical jargon, Savage presents complex concepts in plain English. In addition, a tightly integrated web site contains numerous animations and simulations to further connect the seat of the reader's intellect to the seat of their pants.The Flaw of Averages typically results when someone plugs a single number into a spreadsheet to represent an uncertain future quantity. Savage finishes the book with a discussion of the emerging field of Probability Management, which cures this problem though a new technology that can pack thousands of numbers into a single spreadsheet cell.Praise for The Flaw of Averages"Statistical uncertainties are pervasive in decisions we make every day in business, government, and our personal lives. Sam Savage's lively and engaging book gives any interested reader the insight and the tools to deal effectively with those uncertainties. I highly recommend The Flaw of Averages." --William J. Perry, Former U.S. Secretary of Defense"Enterprise analysis under uncertainty has long been an academic ideal. . . . In this profound and entertaining book, Professor Savage shows how to make all this practical, practicable, and comprehensible." ---Harry Markowitz, Nobel Laureate in Economics

    What hasn't changed, however, are the basics behind creating a powerful visual - what to say, why to say it, and how to say it for the most impact. In Say It With Charts, Fourth Edition --the latest, cutting-edge edition of his best-selling presentation guide -- Gene Zelazny reveals time-tested tips for preparing effective presentations. Then, this presentation guru shows you how to combine those tips with today's hottest technologies for sharper, stronger visuals. Look to this comprehensive presentation encyclopedia for information on:* How to prepare different types of charts -- pie, bar, column, line, or dot -- and when to use each * Lettering size, color choice, appropriate chart types, and more * Techniques for producing dramatic eVisuals using animation, scanned images, sound, video, and links to pertinent websites

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.Explore the importance of storytelling with a simple visualization exerciseLearn how color conveys information that our brains recognize before we're fully aware of itDiscover how the books we buy and the people we associate with reveal clues to our deeper selvesRecognize a method to the madness of air travel with a visualization of civilian air trafficFind out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include:Nick Bilton, Michael E. Driscoll, Jonathan Feinberg, Danyel Fisher, Jessica Hagy, Gregor Hochmuth, Todd Holloway, Noah Iliinsky, Eddie Jabbour, Valdean Klump, Aaron Koblin, Robert Kosara, Valdis Krebs, JoAnn Kuchera-Morin et al., Andrew Odewahn, Adam Perer, Anders Persson, Maximilian Schich, Matthias Shapiro, Julie Steele, Moritz Stefaner, Jer Thorp, Fernanda Viegas, Martin Wattenberg, and Michael Young.