encoderdecoderarkkitehtuurin
Encoder-decoder architecture is a fundamental deep learning model used for sequence-to-sequence tasks. It consists of two main components: an encoder and a decoder. The encoder processes the input sequence and compresses it into a fixed-length vector representation, often called the context vector or thought vector. This vector encapsulates the essential information from the input. The decoder then takes this context vector and generates the output sequence, one element at a time.
The encoder is typically a recurrent neural network (RNN) such as a Long Short-Term Memory (LSTM) or
This architecture is highly versatile and has been applied to a wide range of natural language processing
A significant advancement in encoder-decoder models is the incorporation of attention mechanisms. Attention allows the decoder