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Transformer Explainer
What is a Transformer? Transformer is a neural network architecture that has fundamentally changed the approach to Artificial Intelligence. Transformer was first introduced in the seminal paper "Attention is All You Need" in 2017 and has since become the go-to architecture for deep learning models, powering text-generative models like OpenAI's GPT, Meta's Llama, and Google's Gemini.
The self-attention mechanism enables the model to focus on relevant parts of the input sequence, allowing it to capture complex relationships and dependencies within the data. After the multiple heads of self-attention capture the diverse relationships between the input tokens, the concatenated outputs are passed through the Multilayer Perceptron (MLP) layer to enhance the model's representational capacity. This normalization helps mitigate issues related to internal covariate shift, allowing the model to learn more effectively and reducing the sensitivity to the initial weights.
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