{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/277bdecb284842ffae7b539eb61f1dff\" frameborder=\"0\" width=\"1680\" height=\"1260\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1260,"width":1680,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1260,"thumbnail_width":1680,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/277bdecb284842ffae7b539eb61f1dff-f042bc62a5a84140.gif","duration":252.167,"title":"10-Understanding Deep Learning: The Power of Neural Networks and Space Folding","description":"In 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."}