Search

A problem-solution guide to encountering various NLP tasks utilizing Java open source libraries and cloud-based solutionsKey FeaturesPerform simple-to-complex NLP text processing tasks using modern Java librariesExtract relationships between different text complexities using a problem-solution approachUtilize cloud-based APIs to perform machine translation operationsBook DescriptionNatural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon Web Services (AWS). You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentence, or semantic word.What you will learnExplore how to use tokenizers in NLP processingImplement NLP techniques in machine learning and deep learning applicationsIdentify sentences within text and learn how to train specialized NER modelsLearn how to classify documents and perform sentiment analysisFind semantic similarities between text elements and extract text from a variety of sourcesPreprocess text from a variety of data sourcesLearn how to identify and translate languagesWho this book is forThis book is for data scientists, NLP engineers, and machine learning developers who want to perform their work on linguistic applications faster with the use of popular libraries on JVM machines. This book will help you build real-world NLP applications using a recipe-based approach. Prior knowledge of Natural Language Processing basics and Java programming is expected.

An in-depth look at Black food and the challenges it faces today For Black Americans, the food system is broken. When it comes to nutrition, Black consumers experience an unjust and inequitable distribution of resources. Black Food Matters examines these issues through in-depth essays that analyze how Blackness is contested through food, differing ideas of what makes our sustenance “healthy,” and Black individuals'own beliefs about what their cuisine should be. Primarily written by nonwhite scholars, and framed through a focus on Black agency instead of deprivation, the essays here showcase Black communities fighting for the survival of their food culture. The book takes readers into the real world of Black sustenance, examining animal husbandry practices in South Carolina, the work done by the Black Panthers to ensure food equality, and Black women who are pioneering urban agriculture. These essays also explore individual and community values, the influence of history, and the ongoing struggle to meet needs and affirm Black life. A comprehensive look at Black food culture and the various forms of violence that threaten the future of this cuisine, Black Food Matters centers Blackness in a field that has too often framed Black issues through a white-centric lens, offering new ways to think about access, privilege, equity, and justice. Contributors: Adam Bledsoe, U of Minnesota; Billy Hall; Analena Hope Hassberg, California State Polytechnic U, Pomona; Yuson Jung, Wayne State U; Kimberly Kasper, Rhodes College; Tyler McCreary, Florida State U; Andrew Newman, Wayne State U; Gillian Richards-Greaves, Coastal Carolina U; Monica M. White, U of Wisconsin–Madison; Brian Williams, Mississippi State U; Judith Williams, Florida International U; Psyche Williams-Forson, U of Maryland, College Park; Willie J. Wright, Rutgers U.

Key FeaturesBook DescriptionIf you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.What you will learnDevelop a deep understanding of the basic NLP tasks and how they relate to each otherDiscover and use the available tokenization enginesImplement techniques for end of sentence detectionApply search techniques to find people and things within a documentConstruct solutions to identify parts of speech within sentencesUse parsers to extract relationships between elements of a documentIntegrate basic tasks to tackle more complex NLP problemsWho this book is forIf you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.

Create robust and maintainable Java applications using the functional style of programmingKey FeaturesExplore how you can blend object-oriented and functional programming styles in JavaUse lambda expressions to write flexible and succinct codeA tutorial that strengthens your fundamentals in functional programming techniques to enhance your applicationsBook DescriptionFunctional programming is an increasingly popular technology that allows you to simplify many tasks that are often cumbersome and awkward using an object-oriented approach. It is important to understand this approach and know how and when to apply it. Functional programming requires a different mindset, but once mastered it can be very rewarding. This book simplifies the learning process as a problem is described followed by its implementation using an object-oriented approach and then a solution is provided using appropriate functional programming techniques. Writing succinct and maintainable code is facilitated by many functional programming techniques including lambda expressions and streams. In this book, you will see numerous examples of how these techniques can be applied starting with an introduction to lambda expressions. Next, you will see how they can replace older approaches and be combined to achieve surprisingly elegant solutions to problems. This is followed by the investigation of related concepts such as the Optional class and monads, which offer an additional approach to handle problems. Design patterns have been instrumental in solving common problems. You will learn how these are enhanced with functional techniques. To transition from an object-oriented approach to a functional one, it is useful to have IDE support. IDE tools to refactor, debug, and test functional programs are demonstrated through the chapters. The end of the book brings together many of these functional programming techniques to create a more comprehensive application. You will find this book a very useful resource to learn and apply functional programming techniques in Java.What you will learnUse lambda expressions to simplyfy codeUse function composition to achieve code fluencyApply streams to simply implementations and achieve parallelismIncorporate recursion to support an application's functionalityProvide more robust implementations using OptionalsImplement design patterns with less codeRefactor object-oriented code to create a functional solutionUse debugging and testing techniques specific to functional programsWho this book is forIf you are a Java developer with object-oriented experience and want to use a functional programming approach in your applications, then this book is for you. All you need to get started is familiarity with basic Java object-oriented programming concepts.

In this book, Ashanté M. Reese makes clear the structural forces that determine food access in urban areas, highlighting Black residents'navigation of and resistance to unequal food distribution systems. Linking these local food issues to the national problem of systemic racism, Reese examines the history of the majority-Black Deanwood neighborhood of Washington, D.C. Based on extensive ethnographic fieldwork, Reese not only documents racism and residential segregation in the nation's capital but also tracks the ways transnational food corporations have shaped food availability. By connecting community members'stories to the larger issues of racism and gentrification, Reese shows there are hundreds of Deanwoods across the country.Reese's geographies of self-reliance offer an alternative to models that depict Black residents as lacking agency, demonstrating how an ethnographically grounded study can locate and amplify nuances in how Black life unfolds within the context of unequal food access.

Explore various approaches to organize and extract useful text from unstructured data using JavaKey FeaturesUse deep learning and NLP techniques in Java to discover hidden insights in textWork with popular Java libraries such as CoreNLP, OpenNLP, and MalletExplore machine translation, identifying parts of speech, and topic modelingBook DescriptionNatural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes. You'll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you'll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You'll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You'll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more. By the end of this book, you'll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications. What you will learnUnderstand basic NLP tasks and how they relate to one anotherDiscover and use the available tokenization enginesApply search techniques to find people, as well as things, within a documentConstruct solutions to identify parts of speech within sentencesUse parsers to extract relationships between elements of a documentIdentify topics in a set of documentsExplore topic modeling from a documentWho this book is forNatural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.

Examine the techniques and Java tools supporting the growing field of data scienceKey FeaturesYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesMake your Java applications more capable using machine learningBook Descriptionpara 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside? Understand data science principles with Java support? Discover machine learning and deep learning essentials? Explore data science problems with Java-based solutionsWhat you will learnWho this book is forWith its tutorial approach, this data science book has been written for experienced Java programmers who want to better understand the field of data science and learn how Java supports its underlying techniques. The step-by-step instructional style also makes Java for Data Science ideal for beginners, allowing you to get up and running quickly.

About This BookYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesA highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks.Who This Book Is ForThis course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you!What You Will LearnUnderstand the key concepts of data scienceExplore the data science ecosystem available in JavaWork with the Java APIs and techniques used to perform efficient data analysisFind out how to approach different machine learning problems with JavaProcess unstructured information such as natural language text or images, and create your own searcLearn how to build deep neural networks with DeepLearning4jBuild data science applications that scale and process large amounts of dataDeploy data science models to production and evaluate their performanceIn DetailData science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. In this course, we cover the basic as well as advanced data science concepts. The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization.You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem.Finally, you will see the different ways to deploy the model and evaluate it in production settings.By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles:Java for Data ScienceMastering Java for Data Science

Harness the hidden power of Java to build network-enabled applications with lower network traffic and faster processesKey Features[•]Learn to deliver superior server-to-server communication through the networking channels[•]Gain expertise of the networking features of your own applications to support various network architectures such as client/server and peer-to-peer[•]Explore the issues that impact scalability, affect security, and allow applications to work in a heterogeneous environmentBook DescriptionNetwork-aware applications are becoming more prevalent and play an ever-increasing role in the world today. Connecting and using an Internet-based service is a frequent requirement for many applications. Java provides numerous classes that have evolved over the years to meet evolving network needs. These range from low-level socket and IP-based approaches to those encapsulated in software services. This book explores how Java supports networks, starting with the basics and then advancing to more complex topics. An overview of each relevant network technology is presented followed by detailed examples of how to use Java to support these technologies. We start with the basics of networking and then explore how Java supports the development of client/server and peer-to-peer applications. The NIO packages are examined as well as multitasking and how network applications can address practical issues such as security. A discussion on networking concepts will put many network issues into perspective and let you focus on the appropriate technology for the problem at hand. The examples used will provide a good starting point to develop similar capabilities for many of your network needs What you will learn[•]Connect to other applications using sockets[•]Use channels and buffers to enhance communication between applications[•]Access network services and develop client/server applications[•]Explore the critical elements of peer-to-peer applications and current technologies available[•]Use UDP to perform multicasting[•]Address scalability through the use of core and advanced threading techniques[•]Incorporate techniques into an application to make it more secure[•]Configure and address interoperability issues to enable your applications to work in a heterogeneous environmentWho this book is forLearning Network Programming with Java is oriented to developers who wish to use network technologies to enhance the utility of their applications. You should have a working knowledge of Java and an interest in learning the latest in network programming techniques using Java. No prior experience with network development or special software beyond the Java SDK is needed. Upon completion of the book, beginner and experienced developers will be able to use Java to access resources across a network and the Internet.

When the levees broke in August 2005 as a result of Hurricane Katrina, 80 percent of the city of New Orleans was flooded, with a loss of 134,000 homes and 986 lives. In particular, the devastation hit the vulnerable communities the hardest: the old, the poor and the African American. The disaster exposed the hideous inequality of the city. In response to the disaster numerous plans, designs and projects were proposed.This bold, challenging and informed book gathers together the variety of responses from politicians, writers, architects and planners and searches for the answers of one of the most important issues of our age: How can we plan for the future, creating a more robust and equal place?

Improve your programming through a solid understanding of C pointers and memory management. With this practical book, you'll learn how pointers provide the mechanism to dynamically manipulate memory, enhance support for data structures, and enable access to hardware. Author Richard Reese shows you how to use pointers with arrays, strings, structures, and functions, using memory models throughout the book.Difficult to master, pointers provide C with much flexibility and power—yet few resources are dedicated to this data type. This comprehensive book has the information you need, whether you're a beginner or an experienced C or C++ programmer or developer.Get an introduction to pointers, including the declaration of different pointer typesLearn about dynamic memory allocation, de-allocation, and alternative memory management techniquesUse techniques for passing or returning data to and from functionsUnderstand the fundamental aspects of arrays as they relate to pointersExplore the basics of strings and how pointers are used to support themExamine why pointers can be the source of security problems, such as buffer overflowLearn several pointer techniques, such as the use of opaque pointers, bounded pointers and, the restrict keyword

Includes index.