Foundations of Deep Learning: Transformer Architecture Overview

This lecture introduces the foundational concepts of deep learning, focusing on the Transformer architecture. It begins with a basic definition of neural networks, specifically L-layer feedforward networks, and discusses their structure and function. The instructor explains the representation power of neural networks, emphasizing the importance of weight matrices and nonlinearities. The lecture then transitions to Transformers, highlighting their computational efficiency and the role of attention mechanisms. The instructor elaborates on the architecture's ability to handle sequence modeling tasks, such as translation, and discusses the significance of positional encoding. Key concepts such as multi-headed attention and the dynamics of vector evolution within the architecture are also covered. The lecture concludes with a discussion on the challenges and future directions in understanding the representation power of Transformers compared to other architectures, such as state-based models. Overall, this lecture provides a comprehensive overview of the principles underlying the Transformer architecture and its applications in deep learning.