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Micrograd in Haskell

  • Implement the graph
  • Visualize the graph with Dot
  • Forward pass
  • Backwards pass pass
  • Make a neuron
  • Make an MLP
  • Make a loss function from some data
  • Train the MLP according to the loss function
  • Store the MLP as separate layers (findable neurons)

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An implementation of Karpathy's micrograd in Haskell

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