Dive deep into the intricacies of backpropagation in neural networks with this comprehensive video tutorial. Explore the manual backpropagation process through a 2-layer MLP (with BatchNorm) without relying on PyTorch autograd's loss.backward(). Gain a strong intuitive understanding of gradient flow through the compute graph, covering cross entropy loss, linear layers, tanh activation, batch normalization, and embedding tables. Build competence and intuition around neural network optimization, setting the foundation for confidently innovating and debugging modern neural networks. Engage with hands-on exercises, supplemented by provided code and resources, to reinforce your learning. Discover the historical context and importance of understanding backpropagation while working through practical examples and gaining insights into concepts like Bessel's correction in batch normalization.