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A problem-solution guide to encounter various NLP tasks utilizing
Java open source libraries and cloud-based solutions Key Features
Perform simple-to-complex NLP text processing tasks using modern
Java libraries Extract relationships between different text
complexities using a problem-solution approach Utilize cloud-based
APIs to perform machine translation operations Book
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's 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, sentences, or semantic words. What you
will learn Explore how to use tokenizers in NLP processing
Implement NLP techniques in machine learning and deep learning
applications Identify sentences within the text and learn how to
train specialized NER models Learn how to classify documents and
perform sentiment analysis Find semantic similarities between text
elements and extract text from a variety of sources Preprocess text
from a variety of data sources Learn how to identify and translate
languages Who 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.
Explore various approaches to organize and extract useful text from
unstructured data using Java Key Features Use deep learning and NLP
techniques in Java to discover hidden insights in text Work with
popular Java libraries such as CoreNLP, OpenNLP, and Mallet Explore
machine translation, identifying parts of speech, and topic
modeling Book 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 learn Understand
basic NLP tasks and how they relate to one another Discover and use
the available tokenization engines Apply search techniques to find
people, as well as things, within a document Construct solutions to
identify parts of speech within sentences Use parsers to extract
relationships between elements of a document Identify topics in a
set of documents Explore topic modeling from a document Who 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.
Data collection, processing, analysis, and more About This Book *
Your entry ticket to the world of data science with the stability
and power of Java * Explore, analyse, and visualize your data
effectively using easy-to-follow examples * A 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
For This 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 Learn * Understand the
key concepts of data science * Explore the data science ecosystem
available in Java * Work with the Java APIs and techniques used to
perform efficient data analysis * Find out how to approach
different machine learning problems with Java * Process
unstructured information such as natural language text or images,
and create your own search * Learn how to build deep neural
networks with DeepLearning4j * Build data science applications that
scale and process large amounts of data * Deploy data science
models to production and evaluate their performance In Detail Data
science is concerned with extracting knowledge and insights from a
wide variety of data sources to analyse patterns or predict future
behaviour. It draws from a wide array of disciplines including
statistics, computer science, mathematics, machine learning, and
data mining. In this course, we cover the basic as well as advanced
data science concepts and how they are implemented using the
popular Java tools and libraries.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. This is followed by a discussion of statistical
techniques and more advanced topics including machine learning,
neural networks, and deep learning. 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. You will cover a wide range of topics - from
classification and regression, to dimensionality reduction and
clustering, deep learning and working with Big Data. 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 Science *
Mastering Java for Data Science Style and approach This course
follows a tutorial approach, providing examples of each of the
concepts covered. With a step-by-step instructional style, this
book covers various facets of data science and will get you up and
running quickly.
Examine the techniques and Java tools supporting the growing field
of data science About This Book * Your entry ticket to the world of
data science with the stability and power of Java * Explore,
analyse, and visualize your data effectively using easy-to-follow
examples * Make your Java applications more capable using machine
learning Who This Book Is For This book is for Java developers who
are comfortable developing applications in Java. Those who now want
to enter the world of data science or wish to build intelligent
applications will find this book ideal. Aspiring data scientists
will also find this book very helpful. What You Will Learn *
Understand the nature and key concepts used in the field of data
science * Grasp how data is collected, cleaned, and processed *
Become comfortable with key data analysis techniques * See
specialized analysis techniques centered on machine learning *
Master the effective visualization of your data * Work with the
Java APIs and techniques used to perform data analysis In Detail
Data science is concerned with extracting knowledge and insights
from a wide variety of data sources to analyse patterns or predict
future behaviour. It draws from a wide array of disciplines
including statistics, computer science, mathematics, machine
learning, and data mining. In this book, we cover the important
data science concepts and how they are supported by Java, as well
as the often statistically challenging techniques, to provide you
with an understanding of their purpose and application. The book
starts with an introduction of data science, followed by the basic
data science tasks of data collection, data cleaning, data
analysis, and data visualization. This is followed by a discussion
of statistical techniques and more advanced topics including
machine learning, neural networks, and deep learning. The next
section examines the major categories of data analysis including
text, visual, and audio data, followed by a discussion of resources
that support parallel implementation. The final chapter illustrates
an in-depth data science problem and provides a comprehensive,
Java-based solution. Due to the nature of the topic, simple
examples of techniques are presented early followed by a more
detailed treatment later in the book. This permits a more natural
introduction to the techniques and concepts presented in the book.
Style and approach This book follows a tutorial approach, providing
examples of each of the major concepts covered. With a step-by-step
instructional style, this book covers various facets of data
science and will get you up and running quickly.
Harness the hidden power of Java to build network-enabled
applications with lower network traffic and faster processes About
This Book * 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 environment Who This
Book Is For Learning 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. 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 environment In Detail Network-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. Style
and approach Each network technology's terms and concepts are
introduced first. This is followed up with code examples to explain
these technologies. Many of the examples are supplemented with
alternate Java 8 solutions when appropriate. Knowledge of Java 8 is
not necessary but these examples will help you better understand
the power of Java 8.
Create robust and maintainable Java applications using the
functional style of programming About This Book * Explore how you
can blend object-oriented and functional programming styles in Java
* Use lambda expressions to write flexible and succinct code * A
tutorial that strengthens your fundamentals in functional
programming techniques to enhance your applications Who This Book
Is For If 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. What you will learn * Use lambda expressions to simplyfy
code * Use function composition to achieve code fluency * Apply
streams to simply implementations and achieve parallelism *
Incorporate recursion to support an application's functionality *
Provide more robust implementations using Optionals * Implement
design patterns with less code * Refactor object-oriented code to
create a functional solution * Use debugging and testing techniques
specific to functional programs In Detail Functional 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. Style and approach In this tutorial, each
chapter starts with an introduction to the terms and concepts
covered in that chapter. It quickly progresses to contrast an
object-oriented approach with a functional approach using numerous
code examples.
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