video1.0<iframe src="https://www.loom.com/embed/277bdecb284842ffae7b539eb61f1dff" frameborder="0" width="1680" height="1260" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12601680Loomhttps://www.loom.com12601680https://cdn.loom.com/sessions/thumbnails/277bdecb284842ffae7b539eb61f1dff-f042bc62a5a84140.gif252.167In this video, I explore why deep learning outperforms wide networks by using a geometric understanding of neural networks through space folding. I demonstrate how deep networks can create complex decision boundaries by stacking layers that fold and bend the input space, allowing for hierarchical transformations. The universal approximation theorem shows that while a single hidden layer can approximate any function, deep networks are more efficient and effective in practice. I compare the performance of a shallow wide network with many neurons against a deep narrow network, revealing that the deep network learns complex boundaries significantly better. I encourage you to consider how depth in neural networks enhances representational power and problem-solving capabilities.