Lecture
This lecture focuses on advanced transformer architectures in deep learning, specifically the Swin transformer, HUBERT, and Flamingo. The instructor begins by recapping previous topics, including vision and audio transformers, and their applications in multimodal inputs. The lecture emphasizes the importance of understanding how these models can be utilized for various data types, such as images and text. The Swin transformer is introduced as an efficient model that addresses the challenges of scale in images, while HUBERT is discussed for its capabilities in speech representation learning. The Flamingo architecture is highlighted for its innovative approach to interleaving visual and textual data, enabling complex interactions. The instructor encourages students to apply these concepts to their mini-projects, emphasizing the significance of practical implementation and experimentation. Throughout the lecture, the instructor engages with students, addressing their questions and providing insights into the future of deep learning and its societal implications.