I've been working on various versions of Thinc for a long time --- it's always been the name for the machine learning code powering spaCy. But previous versions

I've been working on various versions of Thinc for a long time --- it's always been the name for the machine learning code powering spaCy. But previous versions really weren't worth using for other developers, and so I never wanted to advertise the library as something people should use. The new version's been mostly rewritten, and this time, my feelings are different :). There's honestly a lot here that DL people should find interesting, even if you're not really looking for new tools atm (which is very understandable). If you first learned deep learning by using PyTorch and TensorFlow, you might find Thinc to be an interestingly different perspective. The backpropagation mechanics are much more exposed, but functional programming means managing the gradients is little trouble. It's also a great way to try out Python's new type annotation features. We designed the library to use type annotations, and have pretty detailed support for numpy semantics. Other nice features include the config system and the wrappers system: you can use models from external libraries as part of a network, and wire different frameworks together. The idea behind Thinc is to work in conjunction with current DL tooling. We want to avoid providing our own optimised engine, making the library much more lightweight and compatible.