It runs on PCs rather than servers and is ideal for small- to mid-sized businesses and households. But Access is still intimidating to learn. It doesn't help that each new version crammed in yet another set of features; so many, in fact, that even the pros don't know where to find them all. Access 2007 breaks this pattern with some of the most dramatic changes users have seen since Office 95. Most obvious is the thoroughly redesigned user interface, with its tabbed toolbar (or "Ribbon") that makes features easy to locate and use. The features list also includes several long-awaited changes. One thing that hasn't improved is Microsoft's documentation. To learn the ins and outs of all the features in Access 2007, Microsoft merely offers online help.Access 2007: The Missing Manual was written from the ground up for this redesigned application. You will learn how to design complete databases, maintain them, search for valuable nuggets of information, and build attractive forms for quick-and-easy data entry. You'll even delve into the black art of Access programming (including macros and Visual Basic), and pick up valuable tricks and techniques to automate common tasks -- even if you've never touched a line of code before. You will also learn all about the new prebuilt databases you can customize to fit your needs, and how the new complex data feature will simplify your life. With plenty of downloadable examples, this objective and witty book will turn an Access neophyte into a true master.

    It was written (and illustrated!) by an all-star team:David Flanagan, bestselling author of programming language "bibles" (including JavaScript: The Definitive Guide and Java in a Nutshell) and committer to the Ruby Subversion repository.Yukihiro "Matz" Matsumoto, creator, designer and lead developer of Ruby and author of Ruby in a Nutshell, which has been expanded and revised to become this book.why the lucky stiff, artist and Ruby programmer extraordinaire. This book begins with a quick-start tutorial to the language, and then explains the language in detail from the bottom up: from lexical and syntactic structure to datatypes to expressions and statements and on through methods, blocks, lambdas, closures, classes and modules. The book also includes a long and thorough introduction to the rich API of the Ruby platform, demonstrating -- with heavily-commented example code -- Ruby's facilities for text processing, numeric manipulation, collections, input/output, networking, and concurrency. An entire chapter is devoted to Ruby's metaprogramming capabilities.The Ruby Programming Language documents the Ruby language definitively but without the formality of a language specification. It is written for experienced programmers who are new to Ruby, and for current Ruby programmers who want to challenge their understanding and increase their mastery of the language.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    You could spend years discovering solutions to them all, step by step as you encounter them. Or you can just look in here. All successful database applications are destined to eventually run into issues scaling up their performance. Peek into the future of your PostgreSQL database's problems today. Know the warning signs to look for, and how to avoid the most common issues before they even happen. Surprisingly, most PostgreSQL database applications evolve in the same way: Choose the right hardware. Tune the operating system and server memory use. Optimize queries against the database, with the right indexes. Monitor every layer, from hardware to queries, using some tools that are inside PostgreSQL and others that are external. Using monitoring insight, continuously rework the design and configuration. On reaching the limits of a single server, break things up; connection pooling, caching, partitioning, and replication can all help handle increasing database workloads. The path to a high performance database system isn't always easy. But it doesn't have to be mysterious with the right guide. This book is a clear, step-by-step guide to optimizing and scaling up PostgreSQL database servers. - Publisher.

    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.

    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.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    The power of an artificial intelligence (AI) comes from its intelligence, not physical strength and laser guns. Humans steer the future not because we're the strongest or the fastest but because we're the smartest. When machines become smarter than humans, we'll be handing them the steering wheel. What promises—and perils—will these powerful machines present? Stuart Armstrong’s new book navigates these questions with clarity and wit.Can we instruct AIs to steer the future as we desire? What goals should we program into them? It turns out this question is difficult to answer! Philosophers have tried for thousands of years to define an ideal world, but there remains no consensus. The prospect of goal-driven, smarter-than-human AI gives moral philosophy a new urgency. The future could be filled with joy, art, compassion, and beings living worthwhile and wonderful lives—but only if we’re able to precisely define what a "good" world is, and skilled enough to describe it perfectly to a computer program.AIs, like computers, will do what we say—which is not necessarily what we mean. Such precision requires encoding the entire system of human values for an AI: explaining them to a mind that is alien to us, defining every ambiguous term, clarifying every edge case. Moreover, our values are fragile: in some cases, if we mis-define a single piece of the puzzle—say, consciousness—we end up with roughly 0% of the value we intended to reap, instead of 99% of the value.Though an understanding of the problem is only beginning to spread, researchers from fields ranging from philosophy to computer science to economics are working together to conceive and test solutions. Are we up to the challenge?A mathematician by training, Armstrong is a Research Fellow at the Future of Humanity Institute (FHI) at Oxford University. His research focuses on formal decision theory, the risks and possibilities of AI, the long term potential for intelligent life (and the difficulties of predicting this), and anthropic (self-locating) probability. Armstrong wrote Smarter Than Us at the request of the Machine Intelligence Research Institute, a non-profit organization studying the theoretical underpinnings of artificial superintelligence.