The first ebook in the series, Microsoft Azure Essentials: Fundamentals of Azure, introduces developers and IT professionals to the wide range of capabilities in Azure. The authors - both Microsoft MVPs in Azure - present both conceptual and how-to content for key areas, including: Azure Websites and Azure Cloud Services Azure Virtual Machines Azure Storage Azure Virtual Networks Databases Azure Active Directory Management tools Business scenarios Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the “Microsoft Azure Essentials” series.

    

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    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.

    The task-based approach teaches readers how to combine HTML and CSS to create sharp and consistent Web pages.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific 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.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Programming courses usually start with theory, but this book lets you jump right into creative and fun projects. It's ideal for anyone who wants to learn basic programming, and serves as a simple introduction to graphics for people with some programming skills.Written by the founders of Processing, this book takes you through the learning process one step at a time to help you grasp core programming concepts. You'll learn how to sketch with code -- creating a program with one a line of code, observing the result, and then adding to it. Join the thousands of hobbyists, students, and professionals who have discovered this free and educational community platform.Quickly learn programming basics, from variables to objectsUnderstand the fundamentals of computer graphicsGet acquainted with the Processing software development environmentCreate interactive graphics with easy-to-follow projectsUse the Arduino open source prototyping platform to control your Processing graphics

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    But just how does the magic happen?In Black Hat Python, the latest from Justin Seitz (author of the best-selling Gray Hat Python), you'll explore the darker side of Python's capabilities writing network sniffers, manipulating packets, infecting virtual machines, creating stealthy trojans, and more. You'll learn how to:Create a trojan command-and-control using GitHubDetect sandboxing and automate common malware tasks, like keylogging and screenshottingEscalate Windows privileges with creative process controlUse offensive memory forensics tricks to retrieve password hashes and inject shellcode into a virtual machineExtend the popular Burp Suite web-hacking toolAbuse Windows COM automation to perform a man-in-the-browser attackExfiltrate data from a network most sneakilyInsider techniques and creative challenges throughout show you how to extend the hacks and how to write your own exploits.When it comes to offensive security, your ability to create powerful tools on the fly is indispensable. Learn how in Black Hat Python."

    Women and Sabarimala is a rare book and is written from a woman’s perspective, explaining the nature of the temple through India’s traditional knowledge systems, such as Ayurveda, Chakras, Tantra and Agama Shastra. At the same time, the author’s personal experiences simplify the understanding of these deep sciences, providing a glimpse into how temples impact the human physiology and, in particular, women’s menstrual cycles. This book will change the way Hindu temples, especially Sabarimala, are perceived and experienced.

    Although it has a long history, the recent discovery (1992) of the body's widespread endocannabinoid system (ECS) has thrust cannabis back into the limelight again as a viable medicine. In 2012 over 2.5 million prescriptions were written for medical marijuana in the United States. CBD and THC are both cannabinoids found in cannabis. However, while THC produces a “high” in the user, CBD does not. And now legal CBD-rich hemp oil is available over-the-counter in all 50 states, without a prescription. It was discovered that the Hemp Family of plants (cannabis, marijuana) is loaded with cannabinoids that stimulate our ECS receptor sites. These sites are found in the brain, organs, glands, connective tissue and immune cells and plays regulatory roles in many physiological processes including appetite, pain-sensation, mood and memory. The primary purpose of this system revolves around maintaining balance in the body. Cannabinoids found in all varieties of cannabis work in harmony with the cannabinoids we naturally produce when our system is functioning properly. It is now coming to light that we may very well be “Endocannabinoid Deficient” and supplementing with Cannabidiol, known as CBD, may provide just what we’re missing to restore optimum health. The health benefits of cannabidiol (CBD) from natural hemp oil is this book's primary focus. It explores the similarities, differences, uses and benefits of hemp, cannabis and medical marijuana along with the interplay of THC and CBD. Their 480 other components are also discussed, such as terpenoids, flavonoids, enzymes, vitamins, etc. Make no mistake about it, the endocannabinoid system, although newly discovered, is just as important as any other bodily system, like the muscular, cardiac, circulatory or digestive system. The ECS requires its own specialized medicine as found in the Hemp Family of plants, which is also known as cannabis, and which includes all strains of marijuana. Here is a list of conditions known and/or being researched that may be helped by cannabinoid therapeutics and supplementation: Acne ADD/ADHD Addiction AIDS ALS (Lou Gehrig's Disease) Alzheimer’s Anorexia Antibiotic Resistance Anxiety Atherosclerosis Arthritis Asthma Autism Bipolar Cancer Colitis/Crohn’s Depression Diabetes Endocrine Disorders Epilepsy/Seizure Fibromyalgia Glaucoma Heart Disease Huntington’s Inflammation Irritable Bowel Kidney Disease Liver Disease Metabolic Syndrome Migraine Mood Disorders Motion Sickness Multiple Sclerosis Nausea Neurodegeneration Neuropathic Pain Obesity OCD Osteoporosis Parkinson’s Prion/Mad Cow Disease PTSD Rheumatism Schizophrenia Sickle Cell Anemia Skin Conditions Sleep Disorders Spinal Cord Injury Stress Stroke/TBI 10% of the proceeds from sales of this book will be donated to the American Cannabis Nurses Association in an effort to bring together nurses, to share, integrate and one day certify nurses in the science of endocannabinoid therapeutics in nursing practice.