How Artificial Neural Networks Learn Concepts (why depth matters)
Channel:
Subscribers:
173,000
Published on ● Video Link: https://www.youtube.com/watch?v=e5xKayCBOeU
7,103I 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
Other Videos By Art of the Problem
Tags:
neural networks
deep learning
chatgpt
deep neural networks
machine learning
network depth
convolutional neural networks
ai
artificial intelligence
latent space
manifolds
why depth matters
deep vs shallow
manifold hypothesis
gpt