This lecture by the instructor on June 24, 2021, delves into the learning dynamics of deep neural networks using linear networks (LNs) for analysis. It covers the formulation of LNs, dynamics of two-layer and multi-layer networks, self-supervised learning, loss computation, and benefits of decoupled initialization. The lecture explores the dynamics of linear networks and their applications, providing insights into the learning dynamics without contrastive pairs.