Course Curriculum

17 Chapters · 17 Lessons

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    Chapter 1: The Rise of Transformer Models in Sequence Learning

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      The Rise of Transformer Models in Sequence Learning

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    Chapter 2: Text Data Preparation for Transformer Models

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      Text Data Preparation for Transformer Models

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    Chapter 3: Building Blocks of Transformer Architecture

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      Building Blocks of Transformer Architecture

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    Chapter 4: Encoder-only Transformer Configurations

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      Encoder-only Transformer Configurations

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    Chapter 5: Generative Transformers and LLM Architectures

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      Generative Transformers and LLM Architectures

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    Chapter 6: Customizing LLMs Using Retrieval-Augmented Generation (RAG)

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      Customizing LLMs Using Retrieval-Augmented Generation (RAG)

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    Chapter 7: Efficient Fine-Tuning Techniques with PEFT and LoRA

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      Efficient Fine-Tuning Techniques with PEFT and LoRA

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    Chapter 8: Orchestrating LLMs with Tools and Memory

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      Orchestrating LLMs with Tools and Memory

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    Chapter 9: Introduction to Vision Transformer Models

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      Introduction to Vision Transformer Models

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    Chapter 10: Vision Transformers for Image Classification

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      Vision Transformers for Image Classification

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    Chapter 11: Object Detection and Segmentation with Transformer Architectures

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      Object Detection and Segmentation with Transformer Architectures

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    Chapter 12: Vision-Language Models and Multimodal LLMs

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      Vision-Language Models and Multimodal LLMs

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    Chapter 13: Real-World Multimodal GenAI Applications

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      Real-World Multimodal GenAI Applications

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    Chapter 14: Image Generation with Vision Transformers

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      Image Generation with Vision Transformers

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    Chapter 15: The Future of GenAI with Transformers

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      The Future of GenAI with Transformers

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    Index

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      Index

About the Course

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!

About the Author

Dr. S. Mahesh Anand is an educator, corporate trainer, and AI consultant with more than 20 years of experience and expertise in these fields. He has trained over 50,000 learners, founded SCS-India, and led programs like “Learn AI with Anand.” An award-winning expert, Dr. Anand continues to inspire through his teaching, research, and his book on AI fundamentals.