You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    In her clear, concise, and informative text, sex educator Violet Blue provides step-by-step instructions for going down on a woman, as well as accurate and up-to-date information on female anatomy and response. In her friendly, witty tone, Blue dispatches fascinating facts and discusses games for lovers, positions, safety, a variety of techniques, erotic book and video recommendations, and tips for effectively communicating with a partner.

    to the end of the Roman Republic (31 B.C.).

    The book has remained continuously in print through many editions and has some more than a quarter of a million copies. It is, as American Artist Magazine calls it, the "artist's bible," an invaluable reference for the painter, sculptor, and printmaker. During the past few years, however, new art movements and new research have led to many changes in the technology of artist's materials. With the assistance of Mayer's window, Bena, and his colleagues, Viking and Steven Sheehan, Director of the Ralph Mayer Center at Yale University, have prepared this latest revision of the book, which is now completely updated and expanded.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    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.

    Bejan explores controversial and relevant issues such as sustainability, water and food supply, fuel, and economy, to critique the state in which the world understands positions of power and freedom. Breaking down concepts such as desire and power, sports health and culture, the state of economy, water and energy, politics and distribution, Bejan uses the language of physics to explain how each system works in order to clarify the meaning of evolution in its broadest scientific sense, moving the reader towards a better understand of the world's systems and the natural evolution of cultural and political development. What Life Is argues that the evolution phenomenon is much broader and older than the evolutionary designs that constitute the biosphere, empowering readers with a new view of the globe, revealing that the urge to have better ideas has the same physical effect as the urge to have better laws and better government. This is evolution explained loudly but also elegantly, forging a path that flows sustainability.

    You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started--whether you're on Windows, OS X, or Linux--author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you'll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on plain text, CSV, HTML/XML, and JSONExplore data, compute descriptive statistics, and create visualizationsManage your data science workflow using DrakeCreate reusable tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelines using GNU ParallelModel data with dimensionality reduction, clustering, regression, and classification algorithms

    Fascinated by the question of uncertainty, Mar�as eschews the solution and prefers to revel in the narrative process itself, and asks the reader to consider the possibility that the truth as we know it isn't nearly as interesting as its own shadow.

    It's a sharp shocker, an eye opener, asking the big questions about what it means to be human, about consciousness and happiness. It'll pull you in and gross out. Exuberantly curious and shamelessly exuberant, Guerilla Science's Zoe Cormier reinvents popular science for a new generation by breaking all the rules. Let's rock.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    Massively erudite, the essays are also brave, unflinching, and wholly personal. In this fiercely original and audacious work, George Steiner tells of seven books which he did not write. Because intimacies and indiscretions were too threatening. Because the topic brought too much pain. Because its emotional or intellectual challenge proved beyond his capacities. The actual themes range widely and defy conventional taboos: the torment of the gifted when they live among--when they confront--the very great; the experience of sex in different languages; a love for animals greater than for human beings; the costly privilege of exile; a theology of emptiness. Yet a unifying perception underlies this diversity. The best we have or can produce is only the tip of the iceberg. Behind every good book, as in a lit shadow, lies the book which remained unwritten, the one that would have failed better.

    This collection contains no material that has not been previously released.

    And who better to guide you through the brewing process than a tea guru with more than forty years of experience under his belt? Stephen Lee, cofounder of Tazo Tea and Stash Tea, turned his attention to fermented tea and founded Kombucha Wonder Drink in 2001. In Kombucha Revolution, Lee reveals the secrets to brewing the perfect batch of kombucha and caring for your very own SCOBY (Symbiotic Culture of Bacteria and Yeast). He also shares his favorite recipes—plus contributions from brewers, bartenders, and chefs like “Kombucha Mamma” Hannah Crum and Wildwood’s Dustin Clark—for infusing your brew with fruits, herbs, and spices, and incorporating it into juices, smoothies, sauces, snacks, sweets, and cocktails. With recipes for Lavender–Green Tea Kombucha, Cranberry Bitters Cocktails, Kombucha Vinegar, Green Smoothies, Kombucha Lime Ceviche,  and Kombucha Pear Sorbet, mixing this healthful brew into your everyday lifestyle has never been so revolutionary.

    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.