Transformer Models
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Building Transformer Models With Pytorch 2 0
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Author : Prem Timsina
language : en
Publisher: BPB Publications
Release Date : 2024-03-08
Building Transformer Models With Pytorch 2 0 written by Prem Timsina and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-08 with Computers categories.
Your key to transformer based NLP, vision, speech, and multimodalities KEY FEATURES ● Transformer architecture for different modalities and multimodalities. ● Practical guidelines to build and fine-tune transformer models. ● Comprehensive code samples with detailed documentation. DESCRIPTION This book covers transformer architecture for various applications including NLP, computer vision, speech processing, and predictive modeling with tabular data. It is a valuable resource for anyone looking to harness the power of transformer architecture in their machine learning projects. The book provides a step-by-step guide to building transformer models from scratch and fine-tuning pre-trained open-source models. It explores foundational model architecture, including GPT, VIT, Whisper, TabTransformer, Stable Diffusion, and the core principles for solving various problems with transformers. The book also covers transfer learning, model training, and fine-tuning, and discusses how to utilize recent models from Hugging Face. Additionally, the book explores advanced topics such as model benchmarking, multimodal learning, reinforcement learning, and deploying and serving transformer models. In conclusion, this book offers a comprehensive and thorough guide to transformer models and their various applications. WHAT YOU WILL LEARN ● Understand the core architecture of various foundational models, including single and multimodalities. ● Step-by-step approach to developing transformer-based Machine Learning models. ● Utilize various open-source models to solve your business problems. ● Train and fine-tune various open-source models using PyTorch 2.0 and the Hugging Face ecosystem. ● Deploy and serve transformer models. ● Best practices and guidelines for building transformer-based models. WHO THIS BOOK IS FOR This book caters to data scientists, Machine Learning engineers, developers, and software architects interested in the world of generative AI. TABLE OF CONTENTS 1. Transformer Architecture 2. Hugging Face Ecosystem 3. Transformer Model in PyTorch 4. Transfer Learning with PyTorch and Hugging Face 5. Large Language Models: BERT, GPT-3, and BART 6. NLP Tasks with Transformers 7. CV Model Anatomy: ViT, DETR, and DeiT 8. Computer Vision Tasks with Transformers 9. Speech Processing Model Anatomy: Whisper, SpeechT5, and Wav2Vec 10. Speech Tasks with Transformers 11. Transformer Architecture for Tabular Data Processing 12. Transformers for Tabular Data Regression and Classification 13. Multimodal Transformers, Architectures and Applications 14. Explore Reinforcement Learning for Transformer 15. Model Export, Serving, and Deployment 16. Transformer Model Interpretability, and Experimental Visualization 17. PyTorch Models: Best Practices and Debugging
Principles And Modeling Of The Power Transformers
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Author : Behrooz Vahidi
language : en
Publisher: Springer Nature
Release Date : 2023-09-23
Principles And Modeling Of The Power Transformers written by Behrooz Vahidi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-23 with Technology & Engineering categories.
This book describes many aspects of power transformers. And it mainly provides valuable knowledges such as two deals with power transformer construction, different types of transformers and connections, power transformer core modelling, and the low-frequency and mid-frequency modelling of transformers. Moreover, it also introduces a new method for high-frequency modelling of transformer which can attract many students learning the power transformer research field. The goal of this book is to educate the postgraduate students and engineers about principals and modeling of the transformers .
Hands On Generative Ai With Transformers And Diffusion Models
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Author : Omar Sanseviero
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-11-22
Hands On Generative Ai With Transformers And Diffusion Models written by Omar Sanseviero 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 2024-11-22 with Computers categories.
Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model and fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train models that can reflect your own unique style
Hugging Face Transformers Essentials
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-05
Hugging Face Transformers Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-05 with Computers categories.
"Hugging Face Transformers Essentials: From Fine-Tuning to Deployment" is an authoritative guide designed for those seeking to harness the power of state-of-the-art transformer models in natural language processing. Bridging the gap between foundational theory and practical application, this book equips readers with the knowledge to leverage Hugging Face's transformative ecosystem, enabling them to implement and optimize these powerful models effectively. Whether you are a beginner taking your first steps into the realm of AI or an experienced practitioner looking to deepen your expertise, this book offers a structured approach to mastering cutting-edge techniques in NLP. Spanning a comprehensive array of topics, the book delves into the mechanics of building, fine-tuning, and deploying transformer models for diverse applications. Readers will explore the intricacies of transfer learning, domain adaptation, and custom training while understanding the vital ethical considerations and implications of responsible AI development. With its meticulous attention to detail and insights into future trends and innovations, this text serves as both a practical manual and a thought-provoking resource for navigating the evolving landscape of AI and machine learning technologies.
Generative Ai Techniques Models And Applications
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Author : Rajan Gupta
language : en
Publisher: Springer Nature
Release Date : 2025-03-26
Generative Ai Techniques Models And Applications written by Rajan Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-26 with Computers categories.
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from: Clear explanations of advanced concepts. Practical implementation insights. Current industry applications. Ethical framework discussions. Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
Transformers For Natural Language Processing
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-25
Transformers For Natural Language Processing written by Denis Rothman 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 2022-03-25 with Computers categories.
OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Book DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.What you will learn Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4 Who this book is for If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!
The Proceedings Of The 11th Frontier Academic Forum Of Electrical Engineering Fafee2024
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Author : Qingxin Yang
language : en
Publisher: Springer Nature
Release Date : 2024-12-02
The Proceedings Of The 11th Frontier Academic Forum Of Electrical Engineering Fafee2024 written by Qingxin Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Technology & Engineering categories.
This book contains the original and refereed research papers presented at the 11th Frontier Academic Forum of Electrical Engineering (FAFEE 2024) held in Chongqing, China. Topics covered include: Power System and New Energy; Motors and Systems; Power Electronics and Electrical Drives; High Voltage and Discharge; Electrical Energy Storage and Application; New Electrical Materials; Advanced Electromagnetic Technology. The papers share the latest findings in the field of electrical engineering, making the book a valuable asset for researchers, engineers and university students, etc.
Transformers For Machine Learning
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Author : Uday Kamath
language : en
Publisher: CRC Press
Release Date : 2022-05-24
Transformers For Machine Learning written by Uday Kamath and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-24 with Computers categories.
Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.
The 17th International Conference Interdisciplinarity In Engineering
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Author : Liviu Moldovan
language : en
Publisher: Springer Nature
Release Date : 2024-04-01
The 17th International Conference Interdisciplinarity In Engineering written by Liviu Moldovan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-01 with Technology & Engineering categories.
This book contains research papers that were accepted for presentation at the 17th International Conference on Interdisciplinarity in Engineering—INTER-ENG 2023, which was held on 5–6 October 2023, in the city of Târgu Mureș, Romania. The general scope of the conference “Towards transition for a more competitive European industry in a smart, safe and sustainable future” is proposing a new approach related to the development of a new generation of smart factories grounded on the manufacturing and assembly process digitalization. It is related to advance manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, manufacturing tools and equipment. It is a leading international professional and scientific forum of great interest for engineers and scientists who can read in this book research works contributions and recent developments as well as current practices in advanced fields of engineering.
Pretrained Transformers For Text Ranking
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Author : Jimmy Lin
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Pretrained Transformers For Text Ranking written by Jimmy Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing (NLP) applications.This book provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in NLP, information retrieval (IR), and beyond. This book provides a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. It covers a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking inmulti-stage architectures and dense retrieval techniques that perform ranking directly. Two themes pervade the book: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this book also attempts to prognosticate where the field is heading.