Natural Language Processing Mit Pytorch
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Natural Language Processing With Pytorch
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Author : Delip Rao
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-01-22
Natural Language Processing With Pytorch written by Delip Rao and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-22 with Computers categories.
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
Natural Language Processing Mit Pytorch
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Author : Delip Rao
language : de
Publisher:
Release Date : 2019-10-31
Natural Language Processing Mit Pytorch written by Delip Rao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.
Transfer Learning For Natural Language Processing
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Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31
Transfer Learning For Natural Language Processing written by Paul Azunre and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
Applied Natural Language Processing With Pytorch 2 0
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Author : Dr. Deepti Chopra
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-01-27
Applied Natural Language Processing With Pytorch 2 0 written by Dr. Deepti Chopra and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-27 with Computers categories.
TAGLINE Unlock the Power of PyTorch 2.0 for Next-Level Natural Language Processing. KEY FEATURES ● Comprehensive coverage of NLP concepts, techniques, and best practices. ● Hands-on examples with code implementations using PyTorch 2.0. ● Focus on real-world applications and optimizing NLP models. ● Learn to develop advanced NLP solutions with dynamic GPU acceleration. DESCRIPTION Natural Language Processing (NLP) is revolutionizing industries, from chatbots to data insights. PyTorch 2.0 offers the tools to build powerful NLP models. Applied Natural Language Processing with PyTorch 2.0 provides a practical guide to mastering NLP with this advanced framework. This book starts with a strong foundation in NLP concepts and the essentials of PyTorch 2.0, ensuring that you are well-equipped to tackle advanced topics. It covers key techniques such as transformer models, pre-trained language models, sequence-to-sequence models, and more. Each chapter includes hands-on examples and code implementations for real-world application. With a focus on practical use cases, the book explores NLP tasks like sentiment analysis, text classification, named entity recognition, machine translation, and text generation. You'll learn how to preprocess text, design neural architectures, train models, and evaluate results. Whether you're a beginner or an experienced professional, this book will empower you to develop advanced NLP models and solutions. Get started today and unlock the potential of NLP with PyTorch 2.0! WHAT WILL YOU LEARN ● Master cutting-edge NLP techniques and integrate PyTorch 2.0 effectively. ● Implement NLP concepts with clear, hands-on examples using PyTorch 2.0. ● Tackle a wide range of NLP tasks, suitable for all experience levels. ● Explore tasks like sentiment analysis, text classification, and translation. ● Leverage advanced deep learning techniques for powerful NLP solutions. ● Preprocess text, design models, train, and evaluate their performance. WHO IS THIS BOOK FOR? This book is ideal for data scientists, machine learning engineers, and NLP practitioners, whether you're a beginner or an experienced professional. A basic understanding of Python and machine learning concepts is recommended, as the book focuses on practical applications, advanced techniques, and integrating PyTorch 2.0 for deep learning-powered NLP solutions. TABLE OF CONTENTS 1. Introduction to Natural Language Processing 2. Getting Started with PyTorch 3. Text Preprocessing 4. Building NLP Models with PyTorch 5. Advanced NLP Techniques with PyTorch 6. Model Training and Evaluation 7. Improving NLP Models with PyTorch 8. Deployment and Productionization 9. Case Studies and Practical Examples 10. Future Trends in Natural Language Processing and PyTorch Index
Natural Language Processing Mit Pytorch
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Author : Delip Rao
language : de
Publisher:
Release Date : 2019
Natural Language Processing Mit Pytorch written by Delip Rao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
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.
Machine Learning With Pytorch And Scikit Learn
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25
Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Hands On Natural Language Processing With Pytorch 1 X
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Author : Thomas Dop
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-09
Hands On Natural Language Processing With Pytorch 1 X written by Thomas Dop and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-09 with Computers categories.
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.
Natural Language Processing In Action Second Edition
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Author : Hobson Lane
language : en
Publisher: Simon and Schuster
Release Date : 2025-02-25
Natural Language Processing In Action Second Edition written by Hobson Lane and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-25 with Computers categories.
Develop your NLP skills from scratch! This revised bestseller now includes coverage of the latest Python packages, Transformers, the HuggingFace packages, and chatbot frameworks.Natural Language Processing in Action has helped thousands of data scientists build machines that understand human language. In this new and revised edition, you'll discover state-of-the art NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. As you go, you'll create projects that can detect fake news, filter spam, and even answer your questions, all built with Python and its ecosystem of data tools. Natural Language Processing in Action, Second Edition is your guide to building software that can read and interpret human language. This new edition is updated to include the latest Python packages and comes with full coverage of cutting-edge models like BERT, GPT-J and HuggingFace transformers.In it, you'll learn to create fun and useful NLP applications such as semantic search engines that are even better than Google, chatbots that can help you write a book, and a multilingual translation program. Soon, you'll be ready to start tackling real-world problems with NLP.
Artificial Intelligence In Surgery Understanding The Role Of Ai In Surgical Practice
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Author : Daniel A. Hashimoto
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-03-08
Artificial Intelligence In Surgery Understanding The Role Of Ai In Surgical Practice written by Daniel A. Hashimoto and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-08 with Medical categories.
Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI.
Deep Learning With Pytorch Lightning
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Author : Kunal Sawarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29
Deep Learning With Pytorch Lightning written by Kunal Sawarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.
Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key FeaturesBecome well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook Description PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging. By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning. What you will learnCustomize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is for This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.