The goal is to inspire photographers of all levels who wish to improve their work, as well as readers interested in deepening their understanding of the art of photography. Each volume is introduced by a student of the featured photographer. In this book, Mary Ellen Mark (1940-2015)--well known for the emotional power of her pictures, be they of people or animals--offers her insight on observing the world and capturing dramatic moments that reveal more than the reality at hand. Through words and pictures, she shares her own creative process and discusses a wide range of issues, from gaining the trust of the subject and taking pictures that are controlled but unforced, to organizing the frame so that every part contributes toward telling the story.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    You'll work through the development of each project, implementing and running the applications while learning new features along the way.Web frameworks are playing a major role in the creation of today's most compelling web applications, because they automate many of the tedious tasks, allowing developers to instead focus on providing users with creative and powerful features. Python developers have been particularly fortunate in this area, having been able to take advantage of Django, a very popular open source web framework whose stated goal is to make it easier to build better web applications more quickly with less code.Practical Django Projects is the first book to introduce this popular framework by way of a series of realworld projects. What you'll learn Capitalize upon Django's welldefined framework architecture to build web applications faster than ever before Learn by doing by working through the creation of three realworld projects, including a content management system, blog, and social networking site Build userfriendly web sites with wellstructured URLs, session tracking, and syndication options Let Django handle tedious tasks such as database interaction while you focus on building compelling applications Who this book is forWeb developers seeking to use the powerful Django framework to build powerful web sites. "

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    For more than a decade, well-known consultant and business professor Wayne Winston has been teaching corporate clients and MBA students the most effective ways to use Microsoft Excel for data analysis, modeling, and decision making. Now this award-winning educator shares the best of his classroom experience in this practical, business-focused guide. Each chapter advances your data analysis and modeling expertise using real-world examples and learn-by-doing exercises. You also get all the book’s problem-and-solution files on CD—for all the practice you need to solve complex problems and work smarter with Excel.Learn how to solve real business problems with Excel!Create best, worst, and most-likely scenarios for sales Calculate how long it would take to recoup a project’s startup costs Plan personal finances, such as computing loan terms or saving for retirement Estimate a product’s demand curve Simulate stock performance over a year Determine which product mix will yield the greatest profits Interpret the effects of price and advertising on sales Assign a dollar value to customer loyalty Manage inventory and order quantities with precision Create customer service queues with short wait times Estimate the probabilities of equipment failure Model business uncertainties Get new perspectives on data with PivotTable dynamic views Help predict quarterly revenue, outcomes of sporting events, presidential elections, and more! On the CD:Practice files for all the book’s exercises Solutions for problem sets Fully searchable eBook A Note Regarding the CD or DVDThe print version of this book ships with a CD or DVD. For those customers purchasing one of the digital formats in which this book is available, we are pleased to offer the CD/DVD content as a free download via O'Reilly Media's Digital Distribution services. To download this content, please visit O'Reilly's web site, search for the title of this book to find its catalog page, and click on the link below the cover image (Examples, Companion Content, or Practice Files). Note that while we provide as much of the media content as we are able via free download, we are sometimes limited by licensing restrictions. Please direct any questions or concerns to booktech@oreilly.com.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    The text relates the fundamentals to three real-world examples: Intel's 8085, Motorola's 6800, and the 6502 chip used by Apple Computers. This edition includes a student version of the TASM cross-assembler software program, experiments for Digital Computer Electronics and more.

    Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

    Praised for providing an engaging balance of thoughtful examples and explanatory discussion, ?best-selling author Walter Savitch explains concepts and techniques in a straightforward style using understandable language and code enhanced by a suite of pedagogical tools.? "Absolute Java "is appropriate for both introductory and intermediate programming courses introducing Java.

    The vision behind Clojure is of a radically simple language framework holding together a sophisticated collection of programming features. Learning Clojure involves much more than just learning the mechanics of the language. To really get Clojure you need to understand the ideas underlying this structure of framework and features. You need this book: an accessible introduction to Clojure that focuses on the ideas behind the language as well as the practical details of writing code.

    After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layerperceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

    But this high-level language is relatively difficult to master, even if you already know the C programming language.The 2nd edition of Practical C++ Programming is a complete introduction to the C++ language for programmers who are learning C++. Reflecting the latest changes to the C++ standard, this 2nd edition takes a useful down-to-earth approach, placing a strong emphasis on how to design clean, elegant code.In short, to-the-point chapters, all aspects of programming are covered including style, software engineering, programming design, object-oriented design, and debugging. It also covers common mistakes and how to find (and avoid) them. End of chapter exercises help you ensure you've mastered the material.Practical C++ Programming thoroughly covers: C++ Syntax Coding standards and style Creation and use of object classes Templates Debugging and optimization Use of the C++ preprocessor File input/output Steve Oualline's clear, easy-going writing style and hands-on approach to learning make Practical C++ Programming a nearly painless way to master this complex but powerful programming language.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    This is not a handbook. It’s more like a history book.Marshall McLuhan once said:We look at the present through a rear‐view mirror. We march backwards into the future.But in the world of web design, we are mostly preoccupied with the here and now. When we think beyond our present moment, it is usually to contemplate the future—to imagine the devices, features, and interfaces that don’t yet exist. We don’t have time to look back upon our past, and yet the history of web design is filled with interesting ideas.The World Wide Web has been around for long enough now that we can begin to evaluate the twists and turns of its evolution. I wrote this book to highlight some of the approaches to web design that have proven to be resilient. I didn’t do this purely out of historical interest (although I am fascinated by the already rich history of our young industry). In learning from the past, I believe we can better prepare for the future.You won’t find any code in here to help you build better websites. But you will find ideas and approaches. Ideas are more resilient than code. I’ve tried to combine the most resilient ideas from the history of web design into an approach for building the websites of the future.I hope you will join me in building a web that lasts; a web that’s resilient.