Natural Language Processing Recipes

Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing. By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. What You Will Learn Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural language generation problems Who This Book Is For Data scientists who want to refresh and learn various concepts of natural language processing through coding exercises.

Fonts & Encodings

This reference is a fascinating and complete guide to using fonts and typography on the Web and across a variety of operating systems and application software. Fonts & Encodings shows you how to take full advantage of the incredible number of typographic options available, with advanced material that covers everything from designing glyphs to developing software that creates and processes fonts. The era of ASCII characters on green screens is long gone, and industry leaders such as Apple, HP, IBM, Microsoft, and Oracle have adopted the Unicode Worldwide Character Standard. Yet, many software applications and web sites still use a host of standards, including PostScript, TrueType, TeX/Omega, SVG, Fontlab, FontForge, Metafont, Panose, and OpenType. This book explores each option in depth, and provides background behind the processes that comprise today’s “digital space for writing”: Part I introduces Unicode, with a brief history of codes and encodings including ASCII. Learn about the morass of the data that accompanies each Unicode character, and how Unicode deals with normalization, the bidirectional algorithm, and the handling of East Asian characters. Part II discusses font management, including installation, tools for activation/deactivation, and font choices for three different systems: Windows, the Mac OS, and the X Window System (Unix). Part III deals with the technical use of fonts in two specific cases: the TeX typesetting system (and its successor, W, which the author co-developed) and web pages. Part IV describes methods for classifying fonts: Vox, Alessandrini, and Panose-1, which is used by Windows and the CSS standard. Learn about existing tools for creating (or modifying) fonts, including FontLab and FontForge, and become familiar with OpenType properties and AAT fonts. Nowhere else will you find the valuable technical information on fonts and typography that software developers, web developers, and graphic artists need to know to get typography and fonts to work properly.

PyTorch Recipes

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you’ll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch. What You Will Learn Master tensor operations for dynamic graph-based calculations using PyTorch Create PyTorch transformations and graph computations for neural networks Carry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNN Build LSTM models in PyTorch Use PyTorch for text processing Who This Book Is For Readers wanting to dive straight into programming PyTorch.

JavaScript Data Structures and Algorithms

Explore data structures and algorithm concepts and their relation to everyday JavaScript development. A basic understanding of these ideas is essential to any JavaScript developer wishing to analyze and build great software solutions. You’ll discover how to implement data structures such as hash tables, linked lists, stacks, queues, trees, and graphs. You’ll also learn how a URL shortener, such as bit.ly, is developed and what is happening to the data as a PDF is uploaded to a webpage. This book covers the practical applications of data structures and algorithms to encryption, searching, sorting, and pattern matching. It is crucial for JavaScript developers to understand how data structures work and how to design algorithms. This book and the accompanying code provide that essential foundation for doing so. With JavaScript Data Structures and Algorithms you can start developing your knowledge and applying it to your JavaScript projects today. What You’ll Learn Review core data structure fundamentals: arrays, linked-lists, trees, heaps, graphs, and hash-table Review core algorithm fundamentals: search, sort, recursion, breadth/depth first search, dynamic programming, bitwise operators Examine how the core data structure and algorithms knowledge fits into context of JavaScript explained using prototypical inheritance and native JavaScript objects/data types Take a high-level look at commonly used design patterns in JavaScript Who This Book Is For Existing web developers and software engineers seeking to develop or revisit their fundamental data structures knowledge; beginners and students studying JavaScript independently or via a course or coding bootcamp.

Practical Recommender Systems

Recommender systems are practically a necessity for keeping a site’s content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows.

Practical Windows Forensics

Over the last few years, the wave of the cybercrime has risen rapidly. We have witnessed many major attacks on the governmental, military, financial, and media sectors. Tracking all these attacks and crimes requires a deep understanding of operating system operations, how to extract evident data from digital evidence, and the best usage of the digital forensic tools and techniques. Regardless of your level of experience in the field of information security in general, this book will fully introduce you to digital forensics. It will provide you with the knowledge needed to assemble different types of evidence effectively, and walk you through the various stages of the analysis process. We start by discussing the principles of the digital forensics process and move on to show you the approaches that are used to conduct analysis. We will then study various tools to perform live analysis, and go through different techniques to analyze volatile and non-volatile data. Who This Book Is For This book targets forensic analysts and professionals who would like to develop skills in digital forensic analysis for the Windows platform. You will acquire proficiency, knowledge, and core skills to undertake forensic analysis of digital data. Prior experience of information security and forensic analysis would be helpful. You will gain knowledge and an understanding of performing forensic analysis with tools especially built for the Windows platform. What You Will Learn Perform live analysis on victim or suspect Windows systems locally or remotely Understand the different natures and acquisition techniques of volatile and non-volatile data. Create a timeline of all the system actions to restore the history of an incident. Recover and analyze data from FAT and NTFS file systems. Make use of various tools to perform registry analysis. Track a system user’s browser and e-mail activities to prove or refute some hypotheses. Get to know how to dump and analyze computer memory.

Mule in Action

Mule is the most widely used open source ESB-with millions of downloads-providing an alternative to expensive commercial options. Mule in Action is acomprehensive tutorial designed for working Java developers. This authoritativebook explores the architecture and the main features of version Mule 2 throughnumerous running examples. It starts with a quick overview of ESB technologyand a bit of Mule history-including the key changes between Mule 1.x andMule 2. Readers learn to configure Mule and then get straight to the goodstuff-putting Mule to work. Because the core of an ESB system is handling message traffic, the book divesinto the way Mule handles data with chapters on sending and receiving, routing,and transforming data. Next, it takes a close look at Mule’s standard componentsand how you can roll out custom ones. The book closes with a set of chapterson the nuts and bolts of working with Mule. Readers can take Mule farther bylearning techniques for testing, performance tuning, BPM orchestration, andeven a touch of Groovy scripting.

Generic Pipelines Using Docker - The DevOps Guide

Create generic pipelines to reduce your overall DevOps workload and allow your team to deliver faster. This book helps you get up to speed on the pros and cons of generic pipeline methodology, and learn to combine shell scripts and Docker to build generic pipelines. In today’s world of micro-services and agile practices, DevOps teams need to move as fast as feature teams. This can be extremely challenging if you’re creating multiple pipelines per application or tech stack. What if your feature teams could utilize a generic pipeline that could build, test, and deploy any application, regardless of tech stack? What if that pipeline was also cloud and platform agnostic? Too good to be true? Well think again! Generic Pipelines Using Docker explores the principles and implementations that allow you to do just that. You will learn from real-world examples and reusable code. After reading this book you will have the knowledge to build generic pipelines that any team can use. What You’ll Learn Explore the pros and cons of generic pipeline methodology Combine shell scripts and Docker to build a generic pipeline Implement a pipeline across CI/CD platforms Build a pipeline that lends itself well to both centralized and federated DevOps teams Construct a modular pipeline with components that can be added, removed, or replaced as needed Who This Book Is For Professionals who use DevOps or are part of a DevOps team, and are seeking ways to streamline their pipelines and drive more deployments while using less code

Machine Learning Applications Using Python

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

Beginning MySQL Database Design and Optimization

* Shows how to take advantage of MySQL’s built-in functions, minimizing the need to process data once it’s been retrieved from the database. * Demonstrates how to write and use advanced and complex queries to cut down on (middleware) application logic, including nested sub-queries and virtual tables (added since MySQL 4.1). * Points out database design do’s and don’ts, including many real-world examples of bad database designs and how the databases were subsequently improved. * Includes a review of MySQL fundamentals and essential theory, such as naming conventions and connections, for quick reference purposes.