Dive deep into the internals of multi-layer perceptrons (MLPs) in this comprehensive video lecture. Explore the statistics of forward pass activations and backward pass gradients, while learning about potential pitfalls in improperly scaled networks. Discover essential diagnostic tools and visualizations for assessing the health of deep networks. Understand the challenges of training deep neural networks and learn about Batch Normalization, a key innovation that simplifies the process. Gain practical insights through code examples, real-world applications, and visualizations. Complete provided exercises to reinforce your understanding of weight initialization and BatchNorm implementation. Follow along as the lecture covers topics such as Kaiming initialization, PyTorch implementation, and various visualization techniques for network analysis.