This is exactly the kind of breakdown I wish I had when first learning neural networks. The balance between mathematical rigor and intuitive explanation is spot-on especially the part about random weight initialization breaking symmetry. It's refreshing to see someone bridge the gap between just coding something and really understanding how it learns. Looking forward to the next part!