Transformers for natural language processing and computer Vision
explore generative AI and large language models with hugging face ChatGPT GPT 4V and DALL E 3
Bibliographic details
Summary
This book provides a comprehensive introduction to transformer-based deep learning models and their applications in natural language processing (NLP), computer vision, and generative artificial intelligence. It explains the architecture and evolution of transformer models, including attention mechanisms, encoder-decoder structures, and large language models (LLMs). The book covers practical implementation using popular frameworks such as Hugging Face Transformers and explores state-of-the-art models including GPT, BERT, Llama, Vision Transformers (ViTs), and multimodal AI systems. Readers learn how to build, train, fine-tune, and deploy transformer models for tasks such as text generation, language understanding, image classification, object detection, and retrieval-augmented generation (RAG). The third edition also introduces recent advances in generative AI, including ChatGPT, GPT-4V, and DALL·E 3, with hands-on examples and real-world applications.