Lecture
This lecture provides an overview of deep learning concepts, focusing on graphs and transformers. It begins with a recap of previous topics, including graph structures and convolutional networks. The instructor discusses the importance of parameter sharing in simple graphs and how this concept relates to convolutional filters. The lecture then transitions to transformers, explaining their architecture and applications in various domains, including vision and audio processing. The instructor highlights the significance of adapting transformers for different data types, emphasizing their versatility in handling multimodal data. The session also covers practical aspects of a mini-project, encouraging students to align their projects with course objectives. The lecture concludes with a discussion on the integration of vision and language models, showcasing their potential in real-world applications. Overall, the session aims to equip students with a high-level understanding of how to leverage deep learning techniques for their projects.