We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT ...

An Encoder-decoder architecture in machine learning efficiently translates one sequence data form to another.

The transformer’s encoder doesn’t just send a final step of encoding to the decoder; it transmits all hidden states and encodings.

The Transformer architecture is made up of two core components: an encoder and a decoder. The encoder contains layers that process input data, like text and images, iteratively layer by layer.

A Solution: Encoder-Decoder Separation The key to addressing these challenges lies in separating the encoder and decoder components of multimodal machine learning models.