Lecture
This lecture discusses the evolution of attention mechanisms leading to the development of transformers, a pivotal architecture in natural language processing. It begins by addressing the limitations of recurrent neural networks (RNNs), particularly their inability to parallelize computations due to dependencies on previous states. The instructor introduces the transformer model as a solution, highlighting its architecture, which consists of encoder and decoder components made up of multiple transformer blocks. Each block utilizes multi-headed attention, allowing for parallel processing of input sequences. The concept of self-attention is explained, demonstrating how it enables the model to compute attention distributions over its own hidden states. The lecture also covers the importance of positional encoding to maintain word order information, a challenge that arises from the non-sequential nature of transformers. Finally, the instructor compares the performance of transformers with traditional RNNs, emphasizing their efficiency and effectiveness in tasks such as machine translation, while also addressing potential disadvantages and ongoing research in the field.