Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

A step-by-step walkthrough of how Transformer neural networks work, using a simple English-to-Spanish translation example. Covers word embedding (converting words to numbers), positional encoding (tracking word order via sine/cosine functions), self-attention (measuring word similarity via dot products and softmax), encoder-decoder attention (linking input and output sequences), and residual connections. Also touches on multi-head attention, layer normalization, and how backpropagation trains the weights. The explanation is beginner-friendly, building intuition before introducing terminology.