Description
Transformer architectures have become the unified foundation of modern AI – powering language models, computer vision systems, and multimodal applications that process text, images, and speech together. Ultimate Multimodal Transformer Models provides a comprehensive, hands-on guide to mastering every major Transformer variant, from foundational encoder-decoder architectures to cutting-edge vision-language models and production GenAI systems.
You begin with the core building blocks of Transformer architecture and text data preparation, then progressively advance through encoder-only models, generative LLMs, RAG, Agentic workflows, and efficient fine-tuning using PEFT, LoRA, and QLoRA. The book then transitions into Vision Transformers, covering ViT, DETR, SAM, CLIP, and Flamingo, before bringing everything together in real-world multimodal applications combining text, vision, and speech using PyTorch and Hugging Face throughout.
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