Overview

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.

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9789355517494

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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.

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