Building Transformer Models with PyTorch 2.0 (notice n° 1556124)
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| 000 -LEADER | |
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| fixed length control field | 03618cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88955333 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251020124027.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251020s2024 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355517494 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88955333 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Timsina, Prem |
| 245 01 - TITLE STATEMENT | |
| Title | Building Transformer Models with PyTorch 2.0 |
| Remainder of title | NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition) |
| Statement of responsibility, etc. | ['Timsina, Prem'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | BPB Publications |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Your key to transformer based NLP, vision, speech, and multimodalitiesKey Features? Transformer architecture for different modalities and multimodalities.? Practical guidelines to build and fine-tune transformer models.? Comprehensive code samples with detailed documentation.DescriptionThis 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 forThis book caters to data scientists, Machine Learning engineers, developers, and software architects interested in the world of generative AI. Table of Contents1. Transformer Architecture2. Hugging Face Ecosystem3. Transformer Model in PyTorch4. Transfer Learning with PyTorch and Hugging Face5. Large Language Models: BERT, GPT-3, and BART6. NLP Tasks with Transformers7. CV Model Anatomy: ViT, DETR, and DeiT8. Computer Vision Tasks with Transformers9. Speech Processing Model Anatomy: Whisper, SpeechT5, and Wav2Vec10. Speech Tasks with Transformers11. Transformer Architecture for Tabular Data Processing12. Transformers for Tabular Data Regression and Classification13. Multimodal Transformers, Architectures and Applications14. Explore Reinforcement Learning for Transformer15. Model Export, Serving, and Deployment16. Transformer Model Interpretability, and Experimental Visualization17. PyTorch Models: Best Practices and Debugging |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Timsina, Prem |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88955333">https://international.scholarvox.com/netsen/book/88955333</a> |
| Electronic format type | text/html |
| Host name | |
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