Version 1.0.0 is the first stable release of DeepLearning.scala, a simple language for creating complex neural networks.
Along with the library, we created a series of tutorials for Scala developers who want to learn deep learning algorithms.
Features in 1.0.0
Differentiable basic types
Like Theano and other deep learning toolkits, DeepLearning.scala allows you to build neural networks from mathematical formulas. It supports floats, doubles, GPU-accelerated N-dimensional arrays, and calculates derivatives of the weights in the formulas.
Neural networks created by DeepLearning.scala support ADT data structures (e.g. HList and Coproduct), and calculate derivatives through these data structures.
Differentiable control flow
Neural networks created by DeepLearning.scala may contains control flows like
case in a regular language. Combined with ADT data structures, you can implement arbitary algorithms inside neural networks, and still keep some of the variables used in the algorithms differentiable and trainable.
Neural networks created by DeepLearning.scala are composable. You can create large networks by combining smaller networks. If two larger networks share some sub-networks, the weights in shared sub-networks trained with one network affect the other network.
Static type system
All of the above features are statically type checked.
DeepLearning.scala is heavily inspired by @MarisaKirisame. Originally, we worked together for a prototype of deep learning framework, then we split our work aprt to this project and DeepDarkFantasy.
@milessabin’s shapeless provides a solid foundation for type-level programming as used in DeepLearning.scala.