Verleger: Packt Publishing
Thushan Ganegedara, Andrei Lopatenko
Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures.The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP.TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models.By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Deborah A. Dahl
Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future.By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Neo4j Graph Data Modeling. Design efficient and flexible databases by optimizing the power of Neo4j
Mahesh Lal, Mahesh K Lal
Nest.js: A Progressive Node.js Framework. Hit the ground running with Nest.js
Greg Magolan, Patrick Housley, Adrien de Peretti,...
Nest.js is a modern web framework built on a Node.js Express server. With the knowledge of how to use this framework, you can give your applications an organized codebase and a well-defined structure.The book begins by showing how to use Nest.js controllers, providers, modules, bootstrapping, and middleware in your applications. You’ll learn to use the authentication feature of Node.js to manage the restriction access in your application, and how to leverage the Dependency Injection pattern to speed up your application development. As you advance through the book, you'll also see how Nest.js uses TypeORM—an Object Relational Mapping (ORM) that works with several relational databases. You’ll use Nest.js microservices to extract part of your application’s business logic and execute it within a separate Nest.js context. Toward the end of the book, you’ll learn to write tests (both unit tests as well as end-to-end ones) and how to check the percentage of the code your tests cover.By the end of this book, you’ll have all the knowledge you need to build your own Nest.js applications.
Sudipta Mukherjee
Generics were added as part of .NET Framework 2.0 in November 2005. Although similar to generics in Java, .NET generics do not apply type erasure but every object has unique representation at run-time. There is no performance hit from runtime casts and boxing conversions, which are normally expensive..NET offers type-safe versions of every classical data structure and some hybrid ones.This book will show you everything you need to start writing type-safe applications using generic data structures available in Generics API. You will also see how you can use several collections for each task you perform. This book is full of practical examples, interesting applications, and comparisons between Generics and more traditional approaches. Finally, each container is bench marked on the basis of performance for a given task, so you know which one to use and when.This book first covers the fundamental concepts such as type safety, Generic Methods, and Generic Containers. As the book progresses, you will learn how to join several generic containers to achieve your goals and query them efficiently using Linq. There are short exercises in every chapter to boost your knowledge.The book also teaches you some best practices, and several patterns that are commonly available in generic code.Some important generic algorithm definitions are present in Power Collection (an API created by Wintellect Inc.) that are missing from .NET framework. This book shows you how to use such algorithms seamlessly with other generic containers.The book also discusses C5 collections. Java Programmers will find themselves at home with this API. This is the closest to JCF. Some very interesting problems are solved using generic containers from .NET framework, C5, and PowerCollection Algorithms ñ a clone of Google Set and Gender Genie for example!The author has also created a website (https://www.consulttoday.com/genguide) for the book where you can find many useful tools, code snippets, and, applications, which are not the part of code-download section