Neural networks #

Manifold assumption #

Neural networks are able to overcome the “curse of dimensionality” essentially by discovering an embedding of high-dimensional data into a manifold with a significantly lower dimension.

  • This very nice guide to convolutional neural networks, including lots of nice intuition of what convolution is and examples with image processing.
  • This post on my personal website about a universal approximation theorem for single-layer neural networks.
  • A nice post by Justin Meiners about when optimisers can outperform neural networks.
  • A nice notebook with lots of interesting information about neural networks and transformers (which power large language models).
  • An animation explanation how convolution works in neural networks.