Explore the intricacies of ResNet architecture in this comprehensive video tutorial. Dive deep into the identity block and convolutional block of Residual Networks, with line-by-line code explanations. Learn why these networks are called "residual" and understand the concept of residue in deep learning. Gain practical insights into ResNet50's structure, including its 48 convolutional layers, MaxPool, and Average Pool layers. Discover how skip connections solve the vanishing gradient problem in deep networks, enabling the training of ultra-deep neural networks with hundreds or thousands of layers. Perfect for those interested in computer vision, deep learning, and advanced neural network architectures.