It's easy to get pulled into using popular platforms like TensorFlow and PyTorch, but there are a number of other great open-source resources that can help you in your AI research. The truth is there is so much interesting work and so many brilliant new tools being developed on a daily basis in open-source artificial intelligence. It can be difficult to keep up with the ever-accelerating developments in AI and deep learning. So, we've taken the time to curate some interesting tools that you may be able to use. In this article, we'll take a look at 7 interesting libraries for doing a wide variety of cutting-edge research in artificial intelligence and related areas. The diversity of the libraries on this list is significant, and if at least one of the libraries isn't an exact fit for your next project (or perhaps an inspiration for one), they are all licensed under permissive open source licenses so you can contribute, fork, and modify these libraries to your heart's content.

It's easy to get pulled into using popular platforms like TensorFlow and PyTorch, but there are a number of other great open-source resources that can help you in your AI research. The truth is there is so much interesting work and so many brilliant new tools being developed on a daily basis in open-source artificial intelligence. It can be difficult to keep up with the ever-accelerating developments in AI and deep learning. So, we've taken the time to curate some interesting tools that you may be able to use. In this article, we'll take a look at 7 interesting libraries for doing a wide variety of cutting-edge research in artificial intelligence and related areas.

DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and differential equations. We demonstrate the ability to incorporate DifferentialEquations.jl-defined differential equation problems into a Flux-defined neural network, and vice versa. The advantages of being able to use the entire DifferentialEquations.jl suite for this purpose is demonstrated by counter examples where simple integration strategies fail, but the sophisticated integration strategies provided by the DifferentialEquations.jl library succeed. This is followed by a demonstration of delay differential equations and stochastic differential equations inside of neural networks. We show high-level functionality for defining neural ordinary differential equations (neural networks embedded into the differential equation) and describe the extra models in the Flux model zoo which includes neural stochastic differential equations. We conclude by discussing the various adjoint methods used for backpropogation of the differential equation solvers. DiffEqFlux.jl is an important contribution to the area, as it allows the full weight of the differential equation solvers developed from decades of research in the scientific computing field to be readily applied to the challenges posed by machine learning and data science.