Applications and Resources

Congratulations! You are now a backprop master. Maybe you've even looked at the haddocks, which has the technical run-down of all of the functions and types in this library. Now what?

  • Check out my Introducing the backprop library blog post where I announced the library to the world. In it, I introduce the library by building and training a full artificial neural network with it, and use it to classify the famous MNIST handwritten digit data set.

  • If you want an even more high-level perspective and inspiration, check out my A Purely Functional Typed Approach to Trainable Models blog series, where I talk about how looking at modeling through the lens of differentiable programming with purely functional typed code can provide new insights and help you develop and train effective models.

  • While they are mostly re-phrasings of the two things above, I also have some example projects as literate haskell files on the github repository for the library. These are also rendered as pdfs for easier reading.

  • If you're doing anything with linear algebra, why not check out the hmatrix-backprop library, which provides the "backprop-lifted" operations that all of the above examples rely on for linear algebra operations?

This is the end of the "end-user" documentation for backprop! The rest of all you need to know to use the library is in the haddocks on hackage.

Check out the sidebar for more technical details on writing manual gradients, optimization and performance, and equipping your library for backprop!