Ultimate Multimodal Transformer Models by S. Mahesh Anand (.ePUB)
File Size: 14.4 MB
Ultimate Multimodal Transformer Models: Master LLMs, Vision Transformers, RAG, AI Agents, Fine-Tuning, and Multimodal AI Systems with PyTorch and Hugging Face by S. Mahesh Anand
Requirements: .ePUB reader, 14.4 MB | True EPUB
Overview: One Architecture. Infinite Intelligence. 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. By the end of the book, you will be proficient to build, fine-tune, and deploy Transformer-based AI systems across text, vision, and multimodal domains with confidence, applying the right architecture and strategy for every real-world use case! This book is tailored for Data Scientists, ML Engineers, AI Researchers, and Computer Vision Engineers who want to build and deploy Transformer-based AI applications. A working knowledge of Python, basic linear algebra, and fundamental deep learning concepts is expected; no prior Transformer experience is required.
Genre: Non-Fiction > Tech & Devices
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