Reese M. Richard
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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 types
- Learn about dynamic memory allocation, de-allocation, and alternative memory management techniques
- Use techniques for passing or returning data to and from functions
- Understand the fundamental aspects of arrays as they relate to pointers
- Explore the basics of strings and how pointers are used to support them
- Examine why pointers can be the source of security problems, such as buffer overflow
- Learn several pointer techniques, such as the use of opaque pointers, bounded pointers and, the restrict keyword
A problem-solution guide to encounter various NLP tasks utilizing Java open source libraries and cloud-based solutions
- 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
Natural 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 for
This 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.
Table of Contents
- Preparing Text for Analysis and Tokenization
- Isolating Sentences Within a Document
- Performing Name Entity Recognition
- Detecting Parts of Speech Using Neural Networks
- Performing Text Classification
- Finding Relationships Within Text
- Language Identification and Translation
- Identifying Semantic Similarities Within Text
- Common Text Processing and Generation Tasks
- Extracting Data for Use in NLP Analysis
- Creating a Chat Bot
- Appendix: Installation and Configuration