A detailed, step-by-step explanation of how Transformer inference works, covering encoder outputs, autoregressive decoding, masked self-attention, cross-attention, and token-by-token generation with clear mathematical intuition.

A complete, step-by-step explanation of the Transformer decoder architecture, covering masked self-attention, cross-attention, feed-forward networks, and the final softmax output using an English-to-Hindi translation example.

Cross attention is a key mechanism in transformer encoder–decoder models that allows the decoder to focus on relevant parts of the input sequence. This guide explains cross attention step by step, compares it with self-attention, and shows how output representations are formed using input context.

A clear, step-by-step explanation of the Transformer encoder architecture, covering tokenization, positional encoding, self-attention, feed-forward networks, residual connections, and why multiple encoder blocks are used.

Unlock the mechanics of Object Detection with our deep dive into R-CNN. Moving beyond simple image classification, this guide explores how machines localize objects using Bounding Boxes, Selective Search, and Support Vector Machines. Whether you are calculating IoU or understanding the transition from sliding windows to smart proposals, this article covers the complete R-CNN architecture and evaluation metrics.

Scaled dot-product attention is a core component of Transformer models, but why do we divide by √dₖ before applying softmax? This article explains the variance growth problem in high-dimensional dot products, the role of scaling in stabilizing softmax, and the mathematical intuition that makes attention training reliable and effective.