The beauty of functional languages in deep learning – Clojure and Haskell
I have spent a lot of time experimenting with the Haskell bindings for TensorFlow and a little time with HLearn. Sorry for a negative opinion, but as much as I don’t particularly like Python, I would suggest to almost anyone that they stick with Python for deep learning for now. In a year or two, I might recommend TensorFlow implemented turtles all the way down in Swift, but let’s wait and see how that project progresses.<p>I have only spent a few evenings playing with Clojure and mxnet and while I appreciate the efforts of the Clojure mxnet subproject team, I think you are still better off for now with Python, TensorFlow, and PyTorch.<p>A little off topic: I had a deep learning example for Armed Bear Common Lisp (implemented in Java) and DeepLearning4j in the last edition of my Common Lisp book. In the latest edition of my book, I removed that example and the chapter that went with it and replaced it with two examples of Lisp code using REST services written in Python, SpaCy, and TensorFlow - I think that is more practical right now; the situation may change in the future.<p>EDIT: I also added REST examples using Python, SpaCy, and TensorFlow to the second edition of my Haskell book.