Topic 2: The Core Innovation: Attention Mechanisms
Topic: Topic 2: The Core Innovation: Attention Mechanisms
Topic 2 The Core Innovation Attention Mechanisms Introduction
Master the transition from RNNs to Transformers. Learn how dot product similarity, Queries, and Keys enable O(1) sequence interaction and parallel computation.
Dot-Product Attention: Geometry, Complexity, and Scaling
Master the algebra of alignment. Define dot products for high-dimensional manifolds, analyze O(1) interaction, and fix gradient vanishing via scaled attention.
Scaled Dot-Product Attention: Math, Variance, and Retrieval
Master the formal derivation of Scaled Dot-Product Attention. Learn the roles of Q, K, and V, variance stabilization, and hardware-efficient implementation.
Scaled Dot-Product Attention: Math, Variance, and Stability
Master the mechanics of attention. Derive dot-product variance, prove how 1/√dk scaling prevents softmax saturation, and analyze the Jacobian to ensure stable gradient flow in Transformers.
Multi-Head Attention: Parallel Latent Subspaces
Master the math of Multi-Head Attention. Learn how subspace decomposition captures complex relationships and analyze self, masked, and cross-attention variants.
Topic 2 The Core Innovation Attention Mechanisms Guided Practice
Derive the 1/sqrt(dk) scaling factor, analyze softmax saturation pathologies, and compare the computational complexity of Self-Attention vs. RNN architectures.