Why depth matters in a neural network (Deep Learning / AI)

I explore the importance of depth in neural networks (deep learning) and how it relates to their ability to learn complex representations using a folding analogy. We'll discuss the concept of a “latent space,“ which is a high-dimensional space where a neural network can learn to represent data in a compressed, efficient way (manifold hypothesis). It should clarify why deep networks are more effective than shallow or single layer networks. We'll explore what neurons are doing individually and as a group to “understand“ perceptions. GPT, chatgpt, openai, geoff hinton