Semantic Embedding Journey – Part 2: Breaking the RNN Bottleneck with Transformers скачать в хорошем качестве

Semantic Embedding Journey – Part 2: Breaking the RNN Bottleneck with Transformers

This session traces the evolution of neural sequence modeling—from traditional RNN encoder–decoder frameworks to the attention-driven Transformer architecture. We’ll cover: Mathematical intuition behind sequence embeddings Please visit www.isanghaminds.com and register your interest. Hidden state propagation in RNNs Bottleneck problem in fixed-length context vectors Introduction of attention mechanisms Multi-head self-attention and positional encoding Why Transformers scale better than recurrent models A must-watch if you want to deeply understand the foundation of modern NLP systems. please visit and register your interest at www.isanghanminds.com