Description: Building Transformer Models with PyTorch 2.0 by Prem Timsina Transformer architecture for different modalities and multimodalities. Practical guidelines to build and fine-tune transformer models. Comprehensive code samples with detailed documentation. FORMAT Paperback CONDITION Brand New Publisher 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. Author Biography Prem Timsina is the Director of Engineering at Mount Sinai Health Systems, where he oversees the development and implementation of Machine Learning Data Products. He has overseen multiple Machine Learning products that have been used as clinical decision support tool at multiple hospitals within New York City. Details ISBN9355517491 Author Prem Timsina Pages 310 Publisher BPB Publications Year 2024 ISBN-13 9789355517494 Format Paperback Publication Date 2024-04-11 Imprint BPB Publications Subtitle NLP, computer vision, and speech processing with PyTorch and Hugging Face Place of Publication New Delhi Country of Publication India Audience General ISBN-10 9355517491 UK Release Date 2024-04-11 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:159567030;
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Book Title: Building Transformer Models with PyTorch 2.0
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