A practical walkthrough of how the Transformer architecture evolved from encoder-decoder to decoder-only models, why attention’s quadratic scaling became a hard wall, and how Mamba’s state space machines are being absorbed into hybrid architectures that dominate production today.

A practical walkthrough of why RNNs hit a fundamental wall with sequential processing and long-range dependencies, and how the Query-Key-Value attention mechanism solves both problems in one elegant step.

This article breaks down the key mathematical concepts behind neural networks, including weights, biases, and activations, with an example of handwritten digit recognition.