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

    Blade Runner, The Matrix, Star Wars, Avatar, Star Trek, Ready Player One and Avengers show us futuristic worlds where holograms, intelligent robots, smart devices, virtual avatars, digital transactions, and universe-scale teleportation work together perfectly, somehow seamlessly combining the virtual and the physical with the mechanical and the biological. Science fiction has done an excellent job describing a vision of the future where the digital and physical merge naturally into one — in a way that just works everywhere, for everyone. However, none of these visionary fictional works go so far as to describe exactly how this would actually be accomplished. While it has inspired many of us to ask the question—How do we enable science fantasy to become....science fact? The Spatial Web achieves this by first describing how exponentially powerful computing technologies are creating a great “Convergence.” How Augmented and Virtual Reality will enable us to overlay our information and imaginations onto the world. How Artificial Intelligence will infuse the environments and objects around us with adaptive intelligence. How the Internet of Things and Robotics will enable our vehicles, appliances, clothing, furniture, and homes to become connected and embodied with the power to see, feel, hear, smell, touch and move things in the world, and how Blockchain and Cryptocurrencies will secure our data and enable real-time transactions between the human, machine and virtual economies of the future. The book then dives deeply into the challenges and shortcomings of the World Wide Web, the rise of fake news and surveillance capitalism in Web 2.0 and the risk of algorithmic terrorism and biological hacking and “fake-reality” in Web 3.0. It raises concerns about the threat that emerging technologies pose in the hands of rogue actors whether human, algorithmic, corporate or state-sponsored and calls for common sense governance and global cooperation. It calls for business leaders, organizations and governments to not only support interoperable standards for software code, but critically, for ethical, and social codes as well. Authors Gabriel René and Dan Mapes describe in vivid detail how a new “spatial” protocol is required in order to connect the various exponential technologies of the 21st century into an integrated network capable of tracking and managing the real-time activities of our cities, monitoring and adjusting the supply chains that feed them, optimizing our farms and natural resources, automating our manufacturing and distribution, transforming marketing and commerce, accelerating our global economies, running advanced planet-scale simulations and predictions, and even bridging the gap between our interior individual reality and our exterior collective one. Enabling the ability for humans, machines and AI to communicate, collaborate and coordinate activities in the world at a global scale and how the thoughtful application of these technologies could lead to an unprecedented opportunity to create a truly global “networked” civilization or "Smart World.” The book artfully shifts between cyberpunk futurism, cautionary tale-telling, and life-affirming call-to-arms. It challenges us to consider the importance of today’s technological choices as individuals, organizations, and as a species, as we face the historic opportunity we have to transform the web, the world, and our very definition of reality.

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    With this hands-on book, you’ll learn Flask from the ground up by developing a complete social blogging application step-by-step. Author Miguel Grinberg walks you through the framework’s core functionality, and shows you how to extend applications with advanced web techniques such as database migration and web service communication.Rather than impose development guidelines as other frameworks do, Flask leaves the business of extensions up to you. If you have Python experience, this book shows you how to take advantage of that creative freedom.- Learn Flask’s basic application structure and write an example app- Work with must-have components—templates, databases, web forms, and email support- Use packages and modules to structure a large application that scales- Implement user authentication, roles, and profiles- Build a blogging feature by reusing templates, paginating item lists, and working with rich text- Use a Flask-based RESTful API to expose app functionality to smartphones, tablets, and other third-party clients- Learn how to run unit tests and enhance application performance- Explore options for deploying your web app to a production server

    But guess what? It really does depend. The key to becoming a great poker player is in knowing exactly what it depends on. At last there’s a book that gives you that answer. Poker is a game of so many variables: table position, flop texture, the number of players in a hand, the personalities of your opponents, and so much more. Decide to Play Great Poker teaches you how to identify and analyze those variables, interchange them within basic game-situation templates, and become knowledgeable, comfortable, and confident in any poker situation. Instead of just dictating a bunch of rules that work only some of the time, this book teaches you to become a great poker thinker and strategist, so that you can expertly navigate any poker challenge that you encounter. Most players think that the goal of poker is to make money. They’re wrong! The goal of poker is to make good decisions. Money is simply the way you measure how well you’re meeting that objective. So if you’re ready to start making world-class decisions at the poker table—and to reap the substantial rewards that those decisions will yield—all you have to do is decide: Decide to Play Great Poker now. You’ll never be vexed by “it depends” again.

    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.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    This step-by-step system only works with understanding and a disciplined plan. Clayton and Natali give you just that by breaking it all down for you in this book. They arm you with the knowledge and inspiration to free yourself from the dead weight of your mortgage so that you can enjoy your monthly income however the heck you want to! Clayton and Natali Morris met while working as TV news broadcasters. Clayton has been a news anchor for over 15 years and Natali has worked for CBS and NBC for most of her career. In 2010 they started a family and got serious about building legacy wealth for their three children, Miles, Ava, and Eve. They podcast, write, and speak around the world about personal finance and financial empowerment in order to help other families like theirs employ the skills they have learned along the way to attain true financial freedom.

    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.

    For nearly twenty years his infamous fund--Tiger Management--was the talk of the town, routinely delivering double-digit performance. This biography will explore this legendary fund manager's role in the development and popularity of hedge funds, examine his investment methodology and strategy, and look at the growth of his fund and his 'Tigers'--individuals who have gone on to great success themselves. * Includes candid interviews of Robertson, his colleagues, and his peers * Uncovers the trading strategies and investment style of a legendary fund manager * Offers a rare glimpse inside the personal world of Julian Robertson READERSHIP: Those with any interest in or knowledge of hedge funds, business readers, investment professionals. Daniel A. Strachman is Managing Director of Answers Company, a New York-based money management firm that offers investment management services to individuals and institutions. He has contributed many articles on investment management and strategies to publications including the New York Post and the Financial Times and is also the author of Getting Started in Hedge Funds (Wiley 2000). Also available by Daniel Strachman, Getting Started in Hedge Funds, 0471316962 Paper. EAN - 9780471323631 Carton Quantity - 41

    In How to Make Big Money in Your Own Small Business, bestselling business author Jeffrey Fox offers sound rules to succeeding in small business, whether you're running a bookstore, consulting business, or restaurant. In short chapters that range from administration and cash flow to marketing and hiring, Fox reminds entrepreneurs what's important and what's not, what makes a business succeed, and what causes it to fail.

    It affects the expansion and acceleration of the universe-- which is why manifesting with it gets you quicker results. In less than 3,000 words, this book will break down into very simple terms the concept of manifesting with dark energy. If you’ve always believed that anything associated with “dark” energy is bad, this intriguing book will turn you into a believer of dark energy’s power.

    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.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills