This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    

    Focused more on concepts than on statistical methods, it shows readers how to properly use statistics to analyze data and demonstrates how computer software is an integral part of this analysis. "Using Statistics" scenarios discuss how statistics is used in a real business setting. Includes contemporary business applications, many with real data sets, and an integrated case that runs throughout chapters. "PHSTAT," a custom designed Excel add-in, is packaged with each book. Introduction and Data Collection. Presenting Data in Tables and Charts. Summarizing and Describing Numerical Data. Basic Probability and Probability Distributions. Sampling Distributions and Confidence Interval Estimation. Fundamentals of Hypothesis Testing: One-Sample Tests. Two-Sample and C-Sample Tests with Numerical Data. Hypothesis Testing with Categorical Data. Statistical Applications in Quality and Productivity Management. The Simple Linear Regression Model and Correlations. Introduction to Multiple Regression. Time Series Analysis. An accessible introduction or refresher on statistics for those in accounting, marketing, management, economics, and finance.

    A principal component of these resources is 'cohesion'. This book studies the cohesion that arises from semantic relations between sentences. Reference from one to the other, repetition of word meanings, the conjunctive force of but, so, then and the like are considered. Further, it describes a method for analysing and coding sentences, which is applied to specimen texts.

    Get ready for hundreds of pages filled with surprising revelations, inside information, obscure trivia, and universal cosmic destruction. This legend of localization doesn’t stink!

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    The book covers all of the new CompTIA Security+ 2008 exam objectives and maps to the new Security+ 2008 exam. This updated edition features many all-new topics, including topics new to the CompTIA exams like cross site scripting, SQL injection, rootkits, and virtualization, as well as topics of increasing importance in the industry as a whole, like the latest breeds of attackers, Wi-Fi Protected Access 2, and Microsoft Windows Vista security.

    The authors help students learn key sociological concepts through real research examples related to the dynamic interplay of race, class, gender, and other social variables.

    Robert J. Darling organizes President Bush's trip to Florida on Sept. 10, 2001, he believes the next couple of days will be quiet. He has no idea that a war is about to begin. The next day, after terrorists crash airliners into the World Trade Center towers and the Pentagon, Maj. Darling rushes to the president's underground chamber at the White House. There, he takes on the task of liaison between the vice president, national security advisor and the Pentagon. He works directly with the National Command Authority, and he's in the room when Vice President Cheney orders two fighter jets to get airborne in order to shoot down United Flight 93. Throughout the attacks, Maj. Darling witnesses the unprecedented actions that leaders are taking to defend America. As Vice President Cheney, Secretary of State Condoleezza Rice, and others make decisions at a lightning pace with little or no deliberation, he's there to lend his support. Follow Darling's story as he becomes a Marine Corps aviator and rises through the ranks to play an incredible role in responding to a crisis that changed the world in 9-11-01: The White House: Twenty-Four Hours inside the President's Bunker.

    This full-color guide will have you blazing through the web, staying entertained with the latest apps and games, watching your favorite TV shows and movies, and, of course, reading magazines, newspapers, and books to indulge all your interests. Whether this is your first tablet or you're upgrading from another device, you'll get to know your Kindle Fire HD and rekindle your love of reading with "Kindle Fire HD For Dummies"!Covers the Kindle Fire HD and original Kindle FireHelps you navigate the interface, customize your settings, get connected, and take advantage of Amazon CloudShows you how to work with the built-in apps and find new ones to to try out from the Amazon AppstoreExplains how to fill your Kindle Fire with cool stuff - from movies and music to videos, photos, apps, e-books, and moreWalks you through using Amazon's incredibly smooth Amazon Silk web browser to find anything you need online and keep in touch with e-mail, Skype, Facebook, and TwitterLight 'er up and unleash the fun with "Kindle Fire HD For Dummies"!

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    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.

    This thorough introduction to second language research provides a comprehensive review of the research into learner language, internal and external factors in language acquisition, individual differences, and classroom second language learning.

    Through computers, the Internet, the media, and even our daily newspapers, we are awash in a seemingly endless stream of charts, maps, infographics, diagrams, and data. Visual Explanations is a navigational guide through this turbulent sea of information. The book is an essential reference for anyone involved in graphic, web, or multimedia design, as well as for educators and lecturers who use graphics in presentations or classes.Jacket design: Dmitry Krasny.Other artwork by Bonnie Scranton, Dmitry Krasny, and Weilin Wu.