Abstract
In this lecture, the limitations of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in effectively processing sequences are emphasized. However, a breakthrough solution known as Transformers is introduced, which addresses these limitations comprehensively. The architecture of Transformers is meticulously described, with a particular emphasis on its fundamental building blocks. These include positional encoding, which captures the sequential information of input data, as well as multi-headed self or cross attention mechanisms that enable the model to capture dependencies between different elements of the sequence. Additionally, the lecture covers important concepts such as residual connections, which aid in the smooth propagation of information through the network, and layer normalization, which ensures stable training and efficient learning. The lecture also delves into the causal self-attention mechanism employed in decoding, enabling the model to generate output sequences in an autoregressive manner. Lastly, a brief mention is made regarding the optimization algorithms used to train Transformers effectively. Overall, this lecture provides a comprehensive understanding of Transformers and its key components, highlighting its ability to overcome the limitations of traditional RNNs and CNNs in sequence processing tasks.
Typical Transformer architecture.
Vision Transformer.
Attention-and-Transformer-Networks-Abstract-v4.1