Informatyka
Mona M, Premkumar Rangarajan
Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production.To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic.Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Nirant Kasliwal
NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP.The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications.We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn.We conclude by deploying these models as REST APIs with Flask.By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.
Thushan Ganegedara
Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Ankur Goel
If you are already using Neo4j in your application and want to learn more about data analysis or database graphs, this is the book for you. This book also caters for your needs if you are looking to migrate your existing application to Neo4j in the future. We assume that you are already familiar with any general purpose programming language and have some familiarity with Neo4j.
Neo4j Graph Data Modelling. Design efficient and flexible databases by optimizing the power of Neo4j
Mahesh K Lal
If you are a developer who wants to understand the fundamentals of modeling data in Neo4j and how it can be used to model full-fledged applications, then this book is for you. Some understanding of domain modeling may be advantageous but is not essential.
Sonal Raj
If you are a professional or enthusiast who has a basic understanding of graphs or has basic knowledge of Neo4j operations, this is the book for you. Although it is targeted at an advanced user base, this book can be used by beginners as it touches upon the basics. So, if you are passionate about taming complex data with the help of graphs and building high performance applications, you will be able to get valuable insights from this book.
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
Neha Shrivastava, Rishabh Verma
With the rise in the number of tools and technologies available today, developers and architects are always exploring ways to create better and smarter solutions. Before, the differences between target platforms was a major roadblock, but that's not the case now. .NET Core 2.0 By Example will take you on an exciting journey to building better software.This book provides fresh and relevant content to .NET Core 2.0 in a succinct format that’s enjoyable to read. It also delivers concepts, along with the implications, design decisions, and potential pitfalls you might face when targeting Linux and Windows systems, in a logical and simple way.With the .NET framework at its center, the book comprises of five varied projects: a multiplayer Tic-tac-toe game; a real-time chat application, Let'sChat; a chatbot; a microservice-based buying-selling application; and a movie booking application. You will start each chapter with a high-level overview of the content, followed by the above example applications described in detail. By the end of each chapter, you will not only be proficient with the concepts, but you’ll also have created a tangible component in the application.By the end of the book, you will have built five solid projects using all the tools and support provided by the .NET Core 2.0 framework.