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.

    D.S. Malik is ideal for a one-semester course focused on data structures. Clearly written with the student in mind, this text focuses on Data Structures and includes advanced topics in C++ such as Linked Lists and the Standard Template Library (STL). This student-friendly text features abundant Programming Examples and extensive use of visual diagrams to reinforce difficult topics. Students will find Dr. Malik's use of complete programming code and clear display of syntax, explanation, and example easy to read and conducive to learning.

    Covers all the topics needed by computer scientists with a sometimes humorous approach that reviewers found refreshing. The goal of the book is to provide a firm understanding of the principles and the big picture of where computer theory fits into the field.

    Code Craft author Pete Goodliffe presents a collection of useful techniques and approaches to the art and craft of programming that will help boost your career and your well-being.Goodliffe presents sound advice that he's learned in 15 years of professional programming. The book's standalone chapters span the range of a software developer's life--dealing with code, learning the trade, and improving performance--with no language or industry bias. Whether you're a seasoned developer, a neophyte professional, or a hobbyist, you'll find valuable tips in five independent categories:Code-level techniques for crafting lines of code, testing, debugging, and coping with complexityPractices, approaches, and attitudes: keep it simple, collaborate well, reuse, and create malleable codeTactics for learning effectively, behaving ethically, finding challenges, and avoiding stagnationPractical ways to complete things: use the right tools, know what "done" looks like, and seek help from colleaguesHabits for working well with others, and pursuing development as a social activity

    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."

    It's certainly no walk in the park. And, depending on where you've been for your information when it comes to SEO, it might be outdated, or just flat-out wrong. Why is that? Search has been evolving at an uncanny rate in recent years. And, if you're not in the know, then you could end up spinning your wheels and wasting valuable and precious time and resources on techniques that no longer work. The main reason for the recent changes: to increase relevancy. Google's sole mission is to provide the most relevant search results at the top of its searches, in the quickest manner possible. But, in recent years, due to some mischievous behavior at the hand of a small group of people, relevancy began to wane. SEO 2016 :: Understanding Google's Algorithm Adjustments The field of SEO has been changing, all led by Google's onslaught of algorithm adjustments that have decimated and razed some sites while uplifting and building others. Since 2011, Google has made it its mission to hunt out and demote spammy sites that sacrifice user-experience, focus on thin content, or simply spend their time trying to trick and deceive their way to the top of its search results. At the same time, Google has increased its reliance on four major components of trust, that work at the heart of its search algorithm: Trust in Age Trust in Authority Trust in Content Relevancy In this book, you'll learn just how each of these affects Google's search results, and just how you can best optimize your site and content to ensure that you're playing by Google's many rules. And, although there have been many algorithm adjustments over the years, four major ones have shaped and forever changed the search engine landscape: Google Panda Google Penguin Google Hummingbird Google Mobilegeddon We'll discuss the nature of these changes and just how each of these algorithm adjustments have shaped the current landscape in search engine optimization. So what does it take to rank your site today? In order to compete at any level in SEO, you have to earn trust - Google's trust that is. But, what does that take? How can we build trust quickly without jumping through all the hoops? SEO is by no means a small feat. It takes hard work applied consistently overtime. There are no overnight success stories when it comes to SEO. But there are certainly ways to navigate the stormy online waters of Google's highly competitive search. Download SEO 2016 :: Learn Search Engine Optimization Lift the veil on Google's complex search algorithm, and understand just what it takes to rank on Google searches today, not yesterday.

     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.

    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.

    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

    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.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    It presents the principles, paradigms, designs and implementations of modern programming languages, and contains increased coverage of the object-oriented programming paradigm. The book also covers semantics and Java.

    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 provides a compact, readable introduction to Git for new users, as well as a reference to common commands and procedures for those of you with Git experience.Written for Git version 1.8.2, this handy task-oriented guide is organized around the basic version control functions you need, such as making commits, fixing mistakes, merging, and searching history.Examine the state of your project at earlier points in timeLearn the basics of creating and making changes to a repositoryCreate branches so many people can work on a project simultaneouslyMerge branches and reconcile the changes among themClone an existing repository and share changes with push/pull commandsExamine and change your repository’s commit historyAccess remote repositories, using different network protocolsGet recipes for accomplishing a variety of common tasks

    Beginning with an introduction to array formulas, this manual examines topics such as how they differ from ordinary formulas, the benefits and drawbacks of their use, functions that can and cannot handle array calculations, and array constants and functions. Among the practical applications surveyed include how to extract data from tables and unique lists, how to get results that match any criteria, and how to utilize various methods for unique counts. This book contains 529 screen shots.