Author: Delip Rao Publisher: O'Reilly Media ISBN: 1491978201 Category : Computers Languages : en Pages : 256
Book Description
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
Author: Delip Rao Publisher: ISBN: Category : Languages : de Pages : 250
Book Description
Sprachanwendungen wie Amazon Alexa und Google Translate sind heute allgegenwärtig. Grundlage dafür ist das Natural Language Processing (NLP), das zahllose Möglichkeiten für die Entwicklung intelligenter, Deep-Learning-basierter Anwendungen eröffnet. In diesem Buch lernen Sie die neuesten Techniken zur Verarbeitung von Sprache kennen und nutzen dabei das neue, flexible Deep-Learning-Framework PyTorch. Die Autoren vermitteln Ihnen einen Überblick über NLP-Methoden und Grundkonzepte neuronaler Netze und demonstrieren Ihnen dann, wie Sie Sprachanwendungen mit PyTorch entwickeln. Sie erfahren z.B., wie Sie Einbettungen verwenden, um Wörter, Sätze und Dokumente darzustellen, wie sich Sequenzdaten mit RNNs modellieren und Sequenzvoraussagen und Sequenz-zu-Sequenz-Modelle generieren lassen, und Sie lernen Entwurfsmuster für den Aufbau von produktionsreifen NLP-Systemen kennen.
Author: Delip Rao Publisher: "O'Reilly Media, Inc." ISBN: 149197818X Category : Computers Languages : en Pages : 258
Book Description
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
Author: Thomas Dop Publisher: Packt Publishing Ltd ISBN: 1789805538 Category : Computers Languages : en Pages : 277
Book Description
Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured data Key FeaturesGet to grips with word embeddings, semantics, labeling, and high-level word representations using practical examplesLearn modern approaches to NLP and explore state-of-the-art NLP models using PyTorchImprove your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNsBook Description In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots. By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them. What you will learnUse NLP techniques for understanding, processing, and generating textUnderstand PyTorch, its applications and how it can be used to build deep linguistic modelsExplore the wide variety of deep learning architectures for NLPDevelop the skills you need to process and represent both structured and unstructured NLP dataBecome well-versed with state-of-the-art technologies and exciting new developments in the NLP domainCreate chatbots using attention-based neural networksWho this book is for This PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone interested in building intelligent language applications using both traditional NLP approaches and deep learning architectures. If you’re looking to adopt modern NLP techniques and models for your development projects, this book is for you. Working knowledge of Python programming, along with basic working knowledge of NLP tasks, is required.
Author: Jibin Mathew Publisher: ISBN: 9781789133974 Category : Languages : en Pages :
Book Description
"The main goal of this course is to train you to perform complex NLP tasks (and build intelligent language applications) using Deep Learning with PyTorch. You will build two complete real-world NLP applications throughout the course. The first application is a Sentiment Analyzer that analyzes data to determine whether a review is positive or negative towards a particular movie. You will then create an advanced Neural Translation Machine that is a speech translation engine, using Sequence to Sequence models with the speed and flexibility of PyTorch to translate given text into different languages."--Resource description page.
Author: Sunil Patel Publisher: BPB Publications ISBN: 9389898110 Category : Computers Languages : en Pages : 407
Book Description
Learn how to redesign NLP applications from scratch. KEY FEATURESÊÊ ¥ Get familiar with the basics of any Machine Learning or Deep Learning application. ¥ Understand how does preprocessing work in NLP pipeline. ¥ Use simple PyTorch snippets to create basic building blocks of the network commonly used inÊ NLP.Ê ¥ Learn how to build a complex NLP application. ¥ Get familiar with the advanced embedding technique, Generative network, and Audio signal processing techniques. ÊÊ DESCRIPTIONÊ Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied. This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). This book not only covers the classical concept of text processing but also shares the recent advancements. This book will empower users in designing networks with the least computational and time complexity. This book not only covers basics of Natural Language Processing but also helps in deciphering the logic behind advanced concepts/architecture such as Batch Normalization, Position Embedding, DenseNet, Attention Mechanism, Highway Networks, Transformer models and Siamese Networks. This book also covers recent advancements such as ELMo-BiLM, SkipThought, and Bert. This book also covers practical implementation with step by step explanation of deep learning techniques in Topic Modelling, Text Generation, Named Entity Recognition, Text Summarization, and Language Translation. In addition to this, very advanced and open to research topics such as Generative Adversarial Network and Speech Processing are also covered. WHAT YOU WILL LEARNÊ ¥ Learn how to leveraging GPU for Deep Learning ¥ Learn how to use complex embedding models such as BERT ¥ Get familiar with the common NLP applications. ¥ Learn how to use GANs in NLP ¥ Learn how to process Speech data and implementing it in Speech applications Ê WHO THIS BOOK IS FORÊ This book is a must-read to everyone who wishes to start the career with Machine learning and Deep Learning. This book is also for those who want to use GPU for developing Deep Learning applications. TABLE OF CONTENTSÊÊ 1. Understanding the basics of learning Process 2. Text Processing Techniques 3. Representing Language Mathematically 4. Using RNN for NLP 5. Applying CNN In NLP Tasks 6. Accelerating NLP with Advanced Embeddings 7. Applying Deep Learning to NLP tasks 8. Application of Complex Architectures in NLP 9. Understanding Generative Networks 10. Techniques of Speech Processing 11. The Road Ahead
Author: Denis Rothman Publisher: Packt Publishing Ltd ISBN: 1800568630 Category : Computers Languages : en Pages : 385
Book Description
Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.
Author: Frank Millstein Publisher: Frank Millstein ISBN: Category : Computers Languages : en Pages : 117
Book Description
Natural Language Processing With Python This book is a perfect beginner's guide to natural language processing. It is offering an easy to understand guide to implementing NLP techniques using Python. Natural language processing has been around for more than fifty years, but just recently with greater amounts of data present and better computational powers, it has gained a greater popularity. Given the importance of data, there is no wonder why natural language processing is on the rise. If you are interested in learning more, this book will serve as your best companion on this journey introducing you to this challenging, yet extremely engaging world of automatic manipulation of our human language. It covers all the basics you need to know before you dive deeper into NLP and solving more complex NLP tasks in Python. Here Is a Preview of What You’ll Learn Here… The main challenges of natural language processing The history of natural language processing How natural langauge processing actually works The main natural language processing applications Text preprocessing and noise removal Feature engineering and syntactic parsing Part of speech tagging and named entity extraction Topic modeling and word embedding Text classification problems Working with text data using NLTK Text summarization and sentiment analysis And much, much more... Get this book NOW and learn more about Natural Language Processing With Python!
Author: Chitra Vasudevan Publisher: BPB Publications ISBN: 9388176057 Category : Computers Languages : en Pages : 174
Book Description
The book has been written in such a way that the concepts are explained in detail, giving adequate emphasis on examples. To make clarity of the programming examples, logic is explained properly as well as discussed by using comments in the program itself. The book covers the topics right from the start of the software by using coding in software and writing programs into it. The book features more on practical approach with more examples covering topics from simple to complex one addressing many of the core concepts and advanced topics also. Key Features Basics concepts of PyTorch like CNN architecture, RNN architecture are discussed in a detailed manner.The worked out case studies are also dealt in a detailed manner.Each and every chapter concludes with the observations of PyTorch to facilitate a better understanding of PyTorchAbundant worked out coding examples.Highly self-explanatory and user-friendly approach This book will "e;need to have"e; title for various reasons as articulated below. Gaining Customers by adopting and implementing PyTorch in / projects/programs and in Research Departments.Help in sustaining Customer Relationships as the core of all successful working relationships are two essential characteristics: trust and commitment. To demonstrate their trustworthiness and commitment to customers, progressive suppliers periodically provide evidence to customers of their accomplishments. Help in delivering "e;Superior Value and Getting an Equitable Return"e; as an understanding value in business markets and doing business based on value delivered gives suppliers the means to get an equitable return for their efforts. The essence of customer value management is to deliver superior value and get an equitable return for it, both of which depend on the value of assessment.ContentsIntroduction to PyTorchLinear RegressionConvolution Neural Network (CNN) Recurrent Neural Networks (RNN)PyTorch Datasets
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Author: Delip Rao
Author: Delip Rao
Author: Delip Rao
Author: Thomas Dop
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Author: Pierre M. Nugues
Author: Sunil Patel
Author: Denis Rothman
Author: Frank Millstein
Author: Chitra Vasudevan