What is Encoder Decoder? What is a Transformer model and explain its architecture?

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Encoder Decoder is a neural network architecture used for sequence-to-sequence tasks. Transformer model is a type of neural network architecture that relies entirely on self-attention mechanisms.

Encoder Decoder is commonly used in machine translation tasks where the input sequence is encoded into a fixed-length vector representation by the encoder and then decoded into the target sequence by the decoder.
Transformer model consists of an encoder and a decoder, both of which are composed of multiple layers of self-attention mechanisms and feed-forward neural networks.
The self-attention mechanism allows the model to weigh the importance of different words in the input sequence when generating the output sequence, making it more effective in capturing long-range dependencies.
The Transformer model has been widely used in natural language processing tasks such as language translation, text summarization, and question answering.
Examples of Transformer models include BERT, GPT, and T5.

What is Encoder Decoder? What is a Transformer model and explain its architecture?

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