Transformer Encoder
what is Transformer encoder resources?
The Transformer encoder is a type of neural network architecture that was introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. The Transformer encoder uses self-attention to process input sequences, such as text or speech. Self-attention allows the model to weigh the importance of different parts of the input sequence when making predictions. The Transformer encoder consists of multiple layers, each of which applies a series of operations to the input sequence. These operations typically include self-attention, layer normalization, and feedforward neural networks.