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.

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.

Self-attention models lack an inherent sense of word order. This article explains positional encoding in Transformers from first principles, showing how sine–cosine functions encode absolute and relative positions efficiently and enable sequence understanding.

This post explains self-attention using geometric intuition. By visualizing embeddings, dot products, scaling, and weighted vector sums, we see how contextual embeddings shift based on surrounding words and capture meaning relative to context.

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.

Transformers are the foundation of modern AI systems like ChatGPT, BERT, and Vision Transformers. This article explains what Transformers are, how self-attention works, their historical evolution, impact on NLP and generative AI, advantages, limitations, and future directions—all explained clearly from first principles.