backprop can be compared and contrasted to many other similar libraries with some overlap:
The ad library (and variants like diffhask) support automatic differentiation, but only for homogeneous/monomorphic situations. All values in a computation must be of the same type --- so, your computation might be the manipulation of
Doubles through a
Double -> Doublefunction.
backprop allows you to mix matrices, vectors, doubles, integers, and even key-value maps as a part of your computation, and they will all be backpropagated properly with the help of the
The autograd library is a very close equivalent to backprop, implemented in Python for Python applications. The difference between backprop and autograd is mostly the difference between Haskell and Python --- static types with type inference, purity, etc.
There is a link between backprop and deep learning/neural network libraries like tensorflow, caffe, and theano, which all all support some form of heterogeneous automatic differentiation. Haskell libraries doing similar things include grenade.
These are all frameworks for working with neural networks or other gradient-based optimizations --- they include things like built-in optimizers, methods to automate training data, built-in models to use out of the box. backprop could be used as a part of such a framework, like I described in my A Purely Functional Typed Approach to Trainable Models blog series; however, the backprop library itself does not provide any built in models or optimizers or automated data processing pipelines.