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Understanding Gpt 3
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Download or read book Understanding GPT-3 written by and published by Prakash Kumar. This book was released on 2023-02-02 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers: I. Introduction A. Overview of GPT-3 B. Purpose of the book II. Key Technologies in AI Language Processing A. Neural Networks B. Transfer Learning C. Natural Language Processing D. Generative Models III. Theories and Concepts in AI Language Processing A. Word Embeddings B. Sequence-to-Sequence Models C. Attention Mechanisms D. Generative Adversarial Networks IV. GPT-3 Architecture and Training A. Overview of GPT-3 Architecture B. Training Data and Techniques C. Fine-Tuning GPT-3 V. Applications of GPT-3 A. Chatbots B. Question Answering Systems C. Text Generation D. Language Translation VI. Limitations and Challenges of GPT-3 A. Bias and Fairness B. Quality Control and Verification C. Privacy and Security D. Scalability and Performance VII. Future Directions of AI Language Processing A. Integration with Other Technologies B. Improved Accuracy and Efficiency C. Ethics and Social Responsibility D. Advancements in Generative Models VIII. Conclusion A. Summary of Key Findings B. Implications for the Future of AI Language Processing C. Final Thoughts and Recommendations.
Download or read book Exploring GPT-3 written by Steve Tingiris and published by Packt Publishing Ltd. This book was released on 2021-08-27 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with GPT-3 and the OpenAI API for natural language processing using JavaScript and Python Key FeaturesUnderstand the power of potential GPT-3 language models and the risks involvedExplore core GPT-3 use cases such as text generation, classification, and semantic search using engaging examplesPlan and prepare a GPT-3 application for the OpenAI review process required for publishing a live applicationBook Description Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text that is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API. If you want to get hands-on with leveraging artificial intelligence for natural language processing (NLP) tasks, this easy-to-follow book will help you get started. Beginning with a high-level introduction to NLP and GPT-3, the book takes you through practical examples that show how to leverage the OpenAI API and GPT-3 for text generation, classification, and semantic search. You'll explore the capabilities of the OpenAI API and GPT-3 and find out which NLP use cases GPT-3 is best suited for. You'll also learn how to use the API and optimize requests for the best possible results. With examples focusing on the OpenAI Playground and easy-to-follow JavaScript and Python code samples, the book illustrates the possible applications of GPT-3 in production. By the end of this book, you'll understand the best use cases for GPT-3 and how to integrate the OpenAI API in your applications for a wide array of NLP tasks. What you will learnUnderstand what GPT-3 is and how it can be used for various NLP tasksGet a high-level introduction to GPT-3 and the OpenAI APIImplement JavaScript and Python code examples that call the OpenAI APIStructure GPT-3 prompts and options to get the best possible resultsSelect the right GPT-3 engine or model to optimize for speed and cost-efficiencyFind out which use cases would not be suitable for GPT-3Create a GPT-3-powered knowledge base application that follows OpenAI guidelinesWho this book is for Exploring GPT-3 is for anyone interested in natural language processing or learning GPT-3 with or without a technical background. Developers, product managers, entrepreneurs, and hobbyists looking to get to grips with NLP, AI, and GPT-3 will find this book useful. Basic computer skills are all you need to get the most out of this book.
Book Synopsis Transformers for Natural Language Processing by : Denis Rothman
Download or read book Transformers for Natural Language Processing written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2021-01-29 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Tech Trends in Practice by : Bernard Marr
Download or read book Tech Trends in Practice written by Bernard Marr and published by John Wiley & Sons. This book was released on 2020-04-09 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***BUSINESS BOOK AWARDS - FINALIST 2021*** Discover how 25 powerful technology trends are transforming 21st century businesses How will the latest technologies transform your business? Future Tech Trends in Practice will give you the knowledge of today’s most important technology trends, and how to take full advantage of them to grow your business. The book presents25 real-world technology trends along with their potential contributions to organisational success. You’ll learn how to integrate existing advancements and plan for those that are on the way. In this book, best-selling author, strategic business advisor, and respected futurist Bernard Marr explains the role of technology in providing innovative businesses solutions for companies of varying sizes and across different industries. He covers wide-ranging trends and provides an overview of how companies are using these new and emerging technologies in practice. You, too, can prepare your company for the potential and power of trending technology by examining these and other areas of innovation described in Future Tech Trends in Practice: Artificial intelligence, including machine and deep learning The Internet of Things and the rise of smart devices Self-driving cars and autonomous drones 3D printing and additive manufacturing Blockchain technology Genomics and gene editing Augmented, virtual and mixed reality When you understand the technology trends that are driving success, now and into the future, you’ll be better positioned to address and solve problems within your organisation.
Download or read book Pharmako-AI written by K. Allado-McDowell and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book collects essays, stories, and poems ... [the author] wrote with OpenAI's GPT-3 language model, a neural net that generates text sequences"--Page xi.
Book Synopsis Generative Deep Learning by : David Foster
Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Book Synopsis Geometry of Deep Learning by : Jong Chul Ye
Download or read book Geometry of Deep Learning written by Jong Chul Ye and published by Springer Nature. This book was released on 2022-01-05 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems. Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
Book Synopsis Deep Learning and the Game of Go by : Kevin Ferguson
Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning
Book Synopsis Our Final Invention by : James Barrat
Download or read book Our Final Invention written by James Barrat and published by Macmillan. This book was released on 2013-10-01 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elon Musk named Our Final Invention one of 5 books everyone should read about the future A Huffington Post Definitive Tech Book of 2013 Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence. In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine. Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
Download or read book GPT-3 written by Sandra Kublik and published by Packt Publishing Ltd. This book was released on 2023-02-13 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API is a comprehensive book on the Generative Pre-trained Transformer 3 AI language model, covering its significance, capabilities, and application in creating innovative NLP Products. Key FeaturesExploration of GPT-3: The book explores GPT-3, a powerful language model, and its capabilitiesBusiness applications: The book provides practical knowledge on using GPT-3 to create new business productsExamination of AI trends: The book examines the impact of GPT-3 on emerging creator economy and trends like no-code & AGIBook Description GPT-3 has made creating AI apps simpler than ever. This book provides a comprehensive guide on how to utilize the OpenAI API with ease. It explores imaginative methods of utilizing this tool for your specific needs and showcases successful businesses that have been established through its use. The book is divided into two sections, with the first focusing on the fundamentals of the OpenAI API. The second part examines the dynamic and thriving environment that has arisen around GPT-3. Chapter 1 sets the stage with background information and defining key terms. Chapter 2 goes in-depth into the API, breaking it down into its essential components, explaining their functions and offering best practices. Chapter 3, you will build your first app with GPT-3. Chapter 4 features interviews with the founders of successful GPT-3-based products, who share challenges and insights gained. Chapter 5 examines the perspective of enterprises on GPT-3 and its potential for adoption. The problematic consequences of widespread GPT-3 adoption, such as misapplication and bias, are addressed along with efforts to resolve these issues in Chapter 6. Finally, Chapter 7 delves into the future by exploring the most exciting trends and possibilities as GPT-3 becomes increasingly integrated into the commercial ecosystem. What you will learnLearn the essential components of the OpenAI API along with the best practicesBuild and deploy your first GPT-3 powered applicationLearn from the journeys of industry leaders, startup founders who have built and deployed GPT-3 based products at scaleLook at how enterprises view GPT-3 and its potential for adoption for scalable solutionsNavigating the Consequences of GPT-3 adoption and efforts to resolve themExplore the exciting trends and possibilities of combining models with GPT-3 with No codeWho this book is for This book caters to individuals from diverse backgrounds, not just technical experts. It should be useful to you if you are:A data expert seeking to improve your AI expertiseAn entrepreneur looking to revolutionize the AI industryA business leader seeking to enhance your AI knowledge and apply it to informed decision makingA content creator in the language domain looking to utilize GPT-3's language abilities for creative and imaginative projectsAnyone with an AI idea that was previously deemed technically unfeasible or too costly to execute
Book Synopsis Artificial Intelligence By Example by : Denis Rothman
Download or read book Artificial Intelligence By Example written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2020-02-28 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google TranslateUnderstand chained algorithms combining unsupervised learning with decision treesSolve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graphLearn about meta learning models with hybrid neural networksCreate a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data loggingBuilding conversational user interfaces (CUI) for chatbotsWriting genetic algorithms that optimize deep learning neural networksBuild quantum computing circuitsWho this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.
Book Synopsis Linguistics for the Age of AI by : Marjorie Mcshane
Download or read book Linguistics for the Age of AI written by Marjorie Mcshane and published by MIT Press. This book was released on 2021-03-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.
Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka
Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis God, Human, Animal, Machine by : Meghan O'Gieblyn
Download or read book God, Human, Animal, Machine written by Meghan O'Gieblyn and published by Anchor. This book was released on 2022-07-12 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: A strikingly original exploration of what it might mean to be authentically human in the age of artificial intelligence, from the author of the critically-acclaimed Interior States. • "At times personal, at times philosophical, with a bracing mixture of openness and skepticism, it speaks thoughtfully and articulately to the most crucial issues awaiting our future." —Phillip Lopate “[A] truly fantastic book.”—Ezra Klein For most of human history the world was a magical and enchanted place ruled by forces beyond our understanding. The rise of science and Descartes's division of mind from world made materialism our ruling paradigm, in the process asking whether our own consciousness—i.e., souls—might be illusions. Now the inexorable rise of technology, with artificial intelligences that surpass our comprehension and control, and the spread of digital metaphors for self-understanding, the core questions of existence—identity, knowledge, the very nature and purpose of life itself—urgently require rethinking. Meghan O'Gieblyn tackles this challenge with philosophical rigor, intellectual reach, essayistic verve, refreshing originality, and an ironic sense of contradiction. She draws deeply and sometimes humorously from her own personal experience as a formerly religious believer still haunted by questions of faith, and she serves as the best possible guide to navigating the territory we are all entering.
Book Synopsis UNDERSTANDING CHAT GPT FOR HIGH SCHOOL AND COLLEGE by : LARRY FRIDIE
Download or read book UNDERSTANDING CHAT GPT FOR HIGH SCHOOL AND COLLEGE written by LARRY FRIDIE and published by FRIDAY SERVICES LLC. This book was released on with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTRODUCTION TO CHAT GPT FOR HIGH SCHOOL AND COLLEGE STUDENTS
Download or read book Superintelligence written by Nick Bostrom and published by . This book was released on 2014 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain.
Book Synopsis Natural Language Processing with Transformers, Revised Edition by : Lewis Tunstall
Download or read book Natural Language Processing with Transformers, Revised Edition written by Lewis Tunstall and published by "O'Reilly Media, Inc.". This book was released on 2022-05-26 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
