Peeling Data Structures and Algorithms: * Programming puzzles for interviews * Campus Preparation * Degree/Masters Course Preparation * Instructor's * GATE Preparation * Big job hunters: Microsoft, Google, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Webaroo, De-Shaw, Success Factors, Face book, McAfee and many more * Reference Manual for working people

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    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!

    

    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

    Learn how easy it is to handle data as self-contained JSON-style documents, rather than as records in a relational database.Explore ways that document-oriented storage will work for your projectLearn how MongoDB’s schema-free data model handles documents, collections, and multiple databasesExecute basic write operations, and create complex queries to find data with any criteriaUse indexes, aggregation tools, and other advanced query techniquesLearn about monitoring, security and authentication, backup and repair, and moreSet up master-slave and automatic failover replication in MongoDBUse sharding to scale MongoDB horizontally, and learn how it impacts applicationsGet example applications written in Java, PHP, Python, and Ruby

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    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.

    "Basics of Web Design: HTML5 and CSS3, 2e "covers the basic concepts that web designers need to develop their skills: Introductory Internet and Web concepts Creating web pages with HTML5 Configuring text, color, and page layout with Cascading Style Sheets Configuring images and multimedia on web pages Web design best practices Accessibility, usability, and search engine optimization considerations Obtaining a domain name and web host Publishing to the Web

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    The book begins by explaining what makes MongoDB unique and describing its ideal use cases. A series of tutorials designed for MongoDB mastery then leads into detailed examples for leveraging MongoDB in e-commerce, social networking, analytics, and other common applications.About the TechnologyBig data can mean big headaches. MongoDB is a document-oriented database designed to be flexible, scalable, and very fast, even with big data loads. It's built for high availability, supports rich, dynamic schemas, and lets you easily distribute data across multiple servers.About this BookMongoDB in Action introduces you to MongoDB and the document-oriented database model. This perfectly paced book provides both the big picture you'll need as a developer and enough low-level detail to satisfy a system engineer. Numerous examples will help you develop confidence in the crucial area of data modeling. You'll also love the deep explanations of each feature, including replication, auto-sharding, and deploymentThis book is written for developers. No MongoDB or NoSQL experience required.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.What's InsideIndexes, queries, and standard DB operations Map-reduce for custom aggregations and reporting Schema design patterns Deploying for scale and high availabilityTable of ContentsPART 1 GETTING STARTED A database for the modern web MongoDB through the JavaScript shell Writing programs using MongoDB PART 2 APPLICATION DEVELOPMENT IN MONGODB Document-oriented data Queries and aggregation Updates, atomic operations, and deletes PART 3 MONGODB MASTERY Indexing and query optimization Replication Sharding Deployment and administration

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer. Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. You'll also learn how to:Split problems into discrete components to make them easier to solve Make the most of code reuse with functions, classes, and libraries Pick the perfect data structure for a particular job Master more advanced programming tools like recursion and dynamic memory Organize your thoughts and develop strategies to tackle particular types of problems Although the book's examples are written in C++, the creative problem-solving concepts they illustrate go beyond any particular language; in fact, they often reach outside the realm of computer science. As the most skillful programmers know, writing great code is a creative art—and the first step in creating your masterpiece is learning to Think Like a Programmer.

    Writing code that handles unexpected errors and still works is really hard. Most of us learn by trial and error. This short book removes the uncertainty. With over 100 pages of content and dozens of working examples, you’ll learn everything from the mechanics of how exceptions work to how to design a robust failure management architecture for your app or library. Whether you are a Ruby novice or a seasoned veteran, Exceptional Ruby will help you write cleaner, more resilient Ruby code.

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""