Key to Transformer Self Attention (Context sensitive connections)
731This video demystifies the core insight behind Transformers, moving beyond traditional explanations that get lost in query, key, value matrices and positional encoding. Instead, we'll unravel how a unique kind of layer, capable of adapting its connection weights based on input context, catapults the Transformer's efficiency and processing prowess. Comparing this dynamic nature with static layers in traditional networks, we'll see why Transformers excel in handling complex tasks with fewer layers. Get a visual grasp of how mini networks within layers, known as attention heads, act as information filters, dynamically adjusting to input and enhancing the model's learning capability. This simplified yet insightful explanation aims to shed light on the essence of what makes Transformers a game-changer in the realm of deep learning.