Transformers Explained Visually

# Summary Transformers are a neural network architecture introduced in 2017 that have become fundamental to modern AI, powering text-generation models like GPT, Llama, and Gemini through a self-attention mechanism that captures relationships between words. They operate by predicting the next token in a sequence and consist of three key components: embeddings (converting text to numerical vectors), transformer blocks (processing data through attention and MLP layers), and output probability layers. The embedding process involves tokenizing input text, converting tokens to vectors, and adding positional information to create the numerical representation the model processes.