Transformer Architectures and Multimodal Models

This course explores the foundations and evolution of modern transformer architectures, taking you from early sequence models to advanced multimodal systems that power today’s AI breakthroughs. Combining strong conceptual depth with practical demonstrations, this course provides a structured journey through attention mechanisms, transformer design, efficiency innovations, and large-scale training strategies. You will begin by understanding Recurrent Neural Networks (RNNs), LSTMs, and GRUs—examining their strengths and limitations in modeling sequential data. From there, you’ll transition into attention mechanisms and multi-head attention, uncovering how transformers overcame long-standing challenges like vanishing gradients and long-term dependency modeling. As the course progresses, you’ll build a deep understanding of encoder-decoder architectures, positional encoding techniques such as sinusoidal embeddings and RoPE, and efficiency innovations like Flash Attention, GQA, and Mixture of Experts (MoE). The course then expands into multimodal learning and similarity-based systems. You’ll explore Vision Transformers (ViTs), embedding alignment techniques, contrastive learning, and large-scale distributed training strategies. Through demonstrations and analysis, you’ll see how modern transformer systems scale to massive datasets while maintaining performance and memory efficiency. By the end of this course, you will be able to: • Explain the limitations of traditional RNN-based sequence models and how attention mechanisms address them. • Implement and analyze multi-head attention and transformer encoder-decoder architectures. • Compare positional encoding strategies and understand their impact on model generalization. • Evaluate efficiency techniques such as Flash Attention, GQA, and MoE for scaling transformers. • Understand Vision Transformers and multimodal representation learning. • Apply similarity learning concepts using embeddings and distance metrics. • Des