{"version":1,"type":"rich","provider_name":"Libsyn","provider_url":"https:\/\/www.libsyn.com","height":90,"width":600,"title":"MLG 033 Transformers","description":"Links: Notes and resources at ocdevel.com\/mlg\/33 3Blue1Brown videos: https:\/\/3blue1brown.com\/ Try a walking desk stay healthy & sharp while you learn & code Try Descript audio\/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization\u2014even with attention tweaks\u2014making them inefficient on modern hardware. Breakthrough: \u201cAttention Is All You Need\u201d replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization. Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order. Self-Attention Mechanism Q, K, V Explained: Query (Q): The representation of the token seeking contextual info. Key (K): The representation of tokens being compared against. Value (V): The information to be aggregated based on the attention scores. Multi-Head Attention: Splits Q, K, V into multiple \u201cheads\u201d to capture diverse relationships and nuances across different subspaces. Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly. Masking Causal Masking: In autoregressive models, prevents a token from \u201cseeing\u201d future tokens, ensuring proper generation. Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions. Feed-Forward Networks (MLPs) Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they\u2019re where the \u201cfacts\u201d or learned knowledge really get stored. Depth & Expressivity: Their layered nature deepens the model\u2019s capacity to represent complex patterns. Residual Connections & Normalization Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing\/exploding gradients. Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence. Scalability & Efficiency Considerations Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs. Complexity Trade-offs: Self-attention\u2019s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention. Training Paradigms & Emergent Properties Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm. Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked. Interpretability & Knowledge Distribution Distributed Representation: \u201cFacts\u201d aren\u2019t stored in a single layer but are embedded throughout both attention heads and MLP layers. Debate on Attention: While some see attention weights as interpretable, a growing view is that real \u201cknowledge\u201d is diffused across the network\u2019s parameters. ","author_name":"Machine Learning Guide","author_url":"https:\/\/ocdevel.com\/mlg","html":"