Neural Architecture Hub
A comprehensive guide to Sequential Modeling, Recurrent Networks, and Long Short-Term Memory.
Mastering Sequence Models
Welcome to the definitive resource for understanding how machines process temporal data. This project combines rigorous mathematical theory with interactive visualizations to bridge the gap between equations and intuition.
đšī¸ Interactive Visualizer
Attention is the engine of modern AI. Watch each token query every other token and pull weighted information from their values â the core operation behind GPT, BERT, and beyond.
Scaled Dot-Product Attention
Querying context for: "The"
Waiting to process next sequence step...
Key Insight: Each token produces a Query, Key, and Value vector. The dot product of Q and K determines how much it attends to each other token before pulling from V.
Core Learning Path
Foundations of Recurrence
Understand the basic RNN unit and the concept of "unrolling" through time steps. Learn why standard updates fail on long sequences.
Gating Mechanisms
Deep dive into Sigmoid () and Tanh activations. Discover how these functions act as "valves" to let information in or out.
Advanced Architectures
Explore LSTMs, GRUs, and the transition into Transformer-based Attention mechanisms.
Why Visual Learning?
Standard notation like is precise, but it doesn't capture the flow of data. By using the unrolled animations provided in these docs, you can visualize the gradient flow and understand why certain architectures perform better on specific datasets.