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During this talk, you will learn how to use Haskell's powerful features for abstraction to create a Neural Network library in native Haskell that makes it easy to create complex network architectures in a type-safe and flexible way. Automatic differentiation is used to provide painless gradient descent and easy extension with new components without the need to first compute complicated partial derivatives by hand. Furthermore, the API uses "Pipes" for separation of concerns (data import, training, reporting, termination).
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Neural Nets with Automatic Differentiation