The Transformer is a type of deep learning model
0The Transformer is a type of deep learning model and serves as the foundation for language models like BERT and GPT. Unlike traditional models such as RNNs and LSTMs, which process words sequentially, the Transformer uses a mechanism called Self-Attention that allows it to process all words in a sentence simultaneously. This enables the model to grasp the context of the entire input at once.
The processing flow is straightforward: the input data (such as words or image patches) is first transformed through matrix multiplication with learned weights, followed by non-linear transformations using activation functions like ReLU. This process is repeated across multiple layers to produce the final output. The model then compares the prediction with the correct label using a loss function (e.g., cross-entropy), and calculates the gradient via partial differentiation to update the weights and biases in a direction that minimizes the loss.
In essence, the Transformer is still a deep learning model that repeatedly performs gradient-based optimization through partial derivatives and matrix operations. While the architecture may appear complex, its core principles remain consistent with other neural network models.