C Programming: Language: A Step by Step Beginner's Guide to Learn C Programming in 7 Days


Darrel L. Graham - 2016
    It is a great book, not just for beginning programmers, but also for computer users who would want to have an idea what is happening behind the scenes as they work with various computer programs. In this book, you are going to learn what the C programming language entails, how to write conditions, expressions, statements and even commands, for the language to perform its functions efficiently. You will learn too how to organize relevant expressions so that after compilation and execution, the computer returns useful results and not error messages. Additionally, this book details the data types that you need for the C language and how to present it as well. Simply put, this is a book for programmers, learners taking other computer courses, and other computer users who would like to be versed with the workings of the most popular computer language, C. In this book You'll learn: What Is The C Language? Setting Up Your Local Environment The C Structure and Data Type C Constants and Literals C Storage Classes Making Decisions In C The Role Of Loops In C Programming Functions in C Programming Structures and Union in C Bit Fields and Typedef Within C. C Header Files and Type Casting Benefits Of Using The C Language ...and much more!! Download your copy today! click the BUY button and download it right now!

Natural Language Processing with Python


Steven Bird - 2009
    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.

Deep Learning


Ian Goodfellow - 2016
    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.

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites


Matthew A. Russell - 2011
    You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google

Practical Django Projects


James Bennett - 2008
    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. "

Information Theory, Inference and Learning Algorithms


David J.C. MacKay - 2002
    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

CoffeeScript


Trevor Burnham - 2011
    It provides all of JavaScript's functionality wrapped in a cleaner, more succinct syntax. In the first book on this exciting new language, CoffeeScript guru Trevor Burnham shows you how to hold onto all the power and flexibility of JavaScript while writing clearer, cleaner, and safer code.CoffeeScript: Accelerated JavaScript Development offers a thorough introduction to this new language, starting from the basics. You'll learn to use time-saving features like list comprehensions and splats, organize your code into modules with extensible classes, and deploy your work to multiple environments. Each chapter is example-driven and includes challenging exercises to push your CoffeeScript know-how further. Through the course of the book, you'll build a fast-paced multiplayer word game-writing both the client (with jQuery) and server (with Node.js) in CoffeeScript. And because the two languages are so deeply intertwined, you'll deepen your understanding of JavaScript along the way. CoffeeScript makes it easier than ever to write powerful, standards-compliant JavaScript code. CoffeeScript: Accelerated JavaScript Development lets you start doing it today.

The Art of R Programming: A Tour of Statistical Software Design


Norman Matloff - 2011
    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

Programming F# 3.0


Chris Smith - 2009
    You’ll quickly discover the many advantages of the language, including access to all the great tools and libraries of the .NET platform.Reap the benefits of functional programming for your next project, whether you’re writing concurrent code, or building data- or math-intensive applications. With this comprehensive book, former F# team member Chris Smith gives you a head start on the fundamentals and walks you through advanced concepts of the F# language.Learn F#’s unique characteristics for building applicationsGain a solid understanding of F#’s core syntax, including object-oriented and imperative stylesMake your object-oriented code better by applying functional programming patternsUse advanced functional techniques, such as tail-recursion and computation expressionsTake advantage of multi-core processors with asynchronous workflows and parallel programmingUse new type providers for interacting with web services and information-rich environmentsLearn how well F# works as a scripting language

Foundations of Statistical Natural Language Processing


Christopher D. Manning - 1999
    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Digital Computer Electronics


Albert Paul Malvino - 1977
    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.

All-New Amazon Echo User Guide: Newbie to Expert in 1 Hour! (Echo & Alexa)


Tom Edwards - 2015
     From the Number 1 Best Selling authors in Computers and Technology, this clear and concise guide will show you how to get the very best from your new Amazon Echo. Step by step instructions will take you from newbie to expert in just one hour! About the Authors: Tom and Jenna Edwards are the Amazon Tech authors behind the Number 1 Best-selling e-books 250+ Best Kindle Fire HD Apps for the New Kindle Fire Owner and Fire HD 8 & HD 10 User Guide: Newbie to Expert in 2 Hours!

The Little SAS Book: A Primer


Lora D. Delwiche - 1995
    This friendly, easy-to-read guide gently introduces you to the most commonly used features of SAS software plus a whole lot more! Authors Lora Delwiche and Susan Slaughter have revised the text to include concepts of the Output Delivery System; the STYLE= option in the PRINT, REPORT, and TABULATE procedures; ODS HTML, RTF, PRINTER, and OUTPUT destinations; PROC REPORT; more on PROC TABULATE; exporting data; and the colon modifier for informats. You'll find clear and concise explanations of basic SAS concepts (such as DATA and PROC steps), inputting data, modifying and combining data sets, summarizing and presenting data, basic statistical procedures, and debugging SAS programs. Each topic is presented in a self-contained, two-page layout complete with examples and graphics. This format enables new users to get up and running quickly, while the examples allow you to type in the program and see it work!

Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS


John K. Kruschke - 2010
    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

Resilient Web Design


Jeremy Keith
    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.