Transformers Explained Visually

summary

The Transformer is a neural network architecture based on the self-attention mechanism, which allows it to process entire sequences and capture long-range dependencies. It operates on the principle of next-token prediction by converting text into numerical embeddings and processing them through multiple blocks of attention and feed-forward networks. This versatile architecture powers modern AI models like GPT, Llama, and Gemini, moving beyond text to audio, images, and protein structure prediction.

key points

1. The embedding layer converts tokens into high-dimensional vectors and adds positional encoding to maintain the sequence order, allowing the model to understand both meaning and location.
2. The Transformer block uses multi-head self-attention to let tokens communicate and capture contextual relationships, followed by a Multi-Layer Perceptron (MLP) to refine each token's representation independently.
3. The final output is a probability distribution over the vocabulary, which is refined by hyperparameters like temperature, top-k, and top-p to balance deterministic and creative text generation.

who should read it

Beginners in machine learning and developers who want a conceptual and technical breakdown of how generative AI models like GPT-2 work.

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