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.

    The answers aren't easy -- just incredibly successful.

    These people are celebrities now. What was it like when they were just a couple friends with an idea? Founders like Steve Wozniak (Apple), Caterina Fake (Flickr), Mitch Kapor (Lotus), Max Levchin (PayPal), and Sabeer Bhatia (Hotmail) tell you in their own words about their surprising and often very funny discoveries as they learned how to build a company.Where did they get the ideas that made them rich? How did they convince investors to back them? What went wrong, and how did they recover?Nearly all technical people have thought of one day starting or working for a startup. For them, this book is the closest you can come to being a fly on the wall at a successful startup, to learn how it's done.But ultimately these interviews are required reading for anyone who wants to understand business, because startups are business reduced to its essence. The reason their founders become rich is that startups do what businesses do--create value--more intensively than almost any other part of the economy. How? What are the secrets that make successful startups so insanely productive? Read this book, and let the founders themselves tell you.

    Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.

    But it is much more than that, too. It is a public ledger to which everyone has access, but which no single person controls. It allows for companies and individuals to collaborate with an unprecedented degree of trust and transparency. It is cryptographically secure, but fundamentally open. And soon it will be everywhere.In Blockchain Revolution, Don and Alex Tapscott reveal how this game-changing technology will shape the future of the world economy, dramatically improving everything from healthcare records to online voting, and from insurance claims to artist royalty payments. Brilliantly researched and highly accessible, this is the essential text on the next major paradigm shift. Read it, or be left behind.

    They are typically used to categorize something based on other data that you have. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. This book is focused on understanding Random Forests at the conceptual level. Knowing how they work, why they work the way that they do, and what options are available to improve results. This book covers how Random Forests work in an intuitive way, and also explains the equations behind many of the functions, but it only has a small amount of actual code (in python). This book is focused on giving examples and providing analogies for the most fundamental aspects of how random forests and decision trees work. The reason is that those are easy to understand and they stick with you. There are also some really interesting aspects of random forests, such as information gain, feature importances, or out of bag error, that simply cannot be well covered without diving into the equations of how they work. For those the focus is providing the information in a straight forward and easy to understand way.

    It covers topics ranging from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you'll learn how toFight software rot; Avoid the trap of duplicating knowledge; Write flexible, dynamic, and adaptable code; Avoid programming by coincidence; Bullet-proof your code with contracts, assertions, and exceptions; Capture real requirements; Test ruthlessly and effectively; Delight your users; Build teams of pragmatic programmers; and Make your developments more precise with automation. Written as a series of self-contained sections and filled with entertaining anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you'll quickly see improvements in personal productivity, accuracy, and job satisfaction. You'll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You'll become a Pragmatic Programmer.

    This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

    What failed startups don't have are enough customers.Founders and employees fail to spend time thinking about (and working on) traction in the same way they work on building a product. This shortsighted approach has startups trying random tactics - some ads, a blog post or two - in an unstructured way that's guaranteed to fail. This book changes that. Traction Book provides startup founders and employees with the framework successful companies have used to get traction. It allows you to think about which marketing channels make sense for you, given your industry and company stage. This framework has been used by founders like Jimmy Wales (Wikipedia), Alexis Ohanian (Reddit), Paul English (Kayak.com), and Alex Pachikov (Evernote) to build some of the biggest companies and organizations in the world. We interviewed each of the above founders - along with 35+ others - and pulled out the repeatable tactics and strategies they used to get traction. We then cover every possible marketing channel you can use to get traction, and show you which channels will be your key to growth. This book shows you how to grow at a time when getting traction is more important than ever. Below are the channels we cover in the book:Viral Marketing Public Relations (PR) Unconventional PR Search Engine Marketing (SEM) Social and Display Ads Offline Ads Search Engine Optimization (SEO) Content Marketing Email Marketing Engineering as Marketing Target Market Blogs Business Development (BD) Sales Affiliate Programs Existing Platforms Trade Shows Offline Events Speaking Engagements Community BuildingThis book draws on interviews with the following individuals: Jimmy Wales, Co-founder of Wikipedia Alexis Ohanian, Co-founder of reddit Eric Ries, Author of The Lean Startup Rand Fishkin, Founder of SEOmoz Noah Kagan, Founder of AppSumo Patrick McKenzie, CEO of Bingo Card Creator Sam Yagan, Co-founder of OkCupid Andrew Chen, Investor at 500 Startups Justin Kan, Founder of Justin.tv Mark Cramer, CEO of SurfCanyon Colin Nederkoorn, CEO of Customer.io Jason Cohen, Founder of WP Engine Chris Fralic, Partner at First Round Paul English, CEO of Kayak.com Rob Walling, Founder of MicroConf Brian Riley, Co-founder of SlidePad Steve Welch, Co-founder of DreamIt Jason Kincaid, Blogger at TechCrunch Nikhil Sethi, Founder of Adaptly Rick Perreault, CEO of Unbounce Alex Pachikov, Co-founder of Evernote David Skok, Partner at Matrix Ashish Kundra, CEO of myZamana David Hauser, Founder of Grasshopper Matt Monahan, CEO of Inflection Jeff Atwood, Co-founder of Discourse Dan Martell, CEO of Clarity.fm Chris McCann, Founder of StartupDigest Ryan Holiday, Exec at American Apparel Todd Vollmer, Enterprise Sales Veteran Sandi MacPherson, Founder of Quibb Andrew Warner, Founder of Mixergy Sean Murphy, Founder of SKMurphy Satish Dharmaraj, Partner at Redpoint Garry Tan, Partner at Y Combinator Steve Barsh, CEO of Packlate Michael Bodekaer, Co-founder of Smart Launch Zack Linford, Founder of Optimozo

    Six years later they have teamed up again to report that social media is but one of five converging forces that promise to change virtually every aspect of our lives. You know these other forces already: mobile, data, sensors and location-based technology. Combined with social media they form a new generation of personalized technology that knows us better than our closest friends. Armed with that knowledge our personal devices can anticipate what we’ll need next and serve us better than a butler or an executive assistant. The resulting convergent superforce is so powerful that it is ushering in a era the authors call the Age of Context. In this new era, our devices know when to wake us up early because it snowed last night; they contact the people we are supposed to meet with to warn them we’re running late. They even find content worth watching on television. They also promise to cure cancer and make it harder for terrorists to do their damage. Astoundingly, in the coming age you may only receive ads you want to see. Scoble and Israel have spent more than a year researching this book. They report what they have learned from interviewing more than a hundred pioneers of the new technology and by examining hundreds of contextual products. What does it all mean? How will it change society in the future? The authors are unabashed tech enthusiasts, but as they write, an elephant sits in the living room of our book and it is called privacy. We are entering a time when our technology serves us best because it watches us; collecting data on what we do, who we speak with, what we look at. There is no doubt about it: Big Data is watching you. The time to lament the loss of privacy is over. The authors argue that the time is right to demand options that enable people to reclaim some portions of that privacy.

    A simple web search picks out a handful of relevant needles from the world's biggest haystack: the billions of pages on the World Wide Web. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers; and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? This is the first book to answer that question in language anyone can understand, revealing the extraordinary ideas that power our PCs, laptops, and smartphones. Using vivid examples, John MacCormick explains the fundamental "tricks" behind nine types of computer algorithms, including artificial intelligence (where we learn about the "nearest neighbor trick" and "twenty questions trick"), Google's famous PageRank algorithm (which uses the "random surfer trick"), data compression, error correction, and much more. These revolutionary algorithms have changed our world: this book unlocks their secrets, and lays bare the incredible ideas that our computers use every day.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    But how do you build the best team organization for your specific goals, culture, and needs? Team Topologies is a practical, step-by-step, adaptive model for organizational design and team interaction based on four fundamental team types and three team interaction patterns. It is a model that treats teams as the fundamental means of delivery, where team structures and communication pathways are able to evolve with technological and organizational maturity.In Team Topologies, IT consultants Matthew Skelton and Manuel Pais share secrets of successful team patterns and interactions to help readers choose and evolve the right team patterns for their organization, making sure to keep the software healthy and optimize value streams.Team Topologies is a major step forward in organizational design for software, presenting a well-defined way for teams to interact and interrelate that helps make the resulting software architecture clearer and more sustainable, turning inter-team problems into valuable signals for the self-steering organization.

    "This book is a guide to how we do product development at Basecamp. It’s also a toolbox full of techniques that you can apply in your own way to your own process.Whether you’re a founder, CTO, product manager, designer, or developer, you’re probably here because of some common challenges that all software companies have to face."

    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