deeplearning4j-examples

Hi Mark, I want to correct one thing. My company is behind dl4j. We are also the second largest contributor to keras. We have near perfect model import (with keras contributors maintaining it). Dl4j also has TF import and a native graph runner: https://github.com/deeplearning4j/dl4j-examples/tree/master/... Java itself tends to be fairly verbose I know, but not only are there wrappers but for inference if you want to use python for basics and then us for inference. We enable several ways to do that. Folks tend to oversensationlize these "us vs them" framework "fights". Rather than do that, why not just use what's good for each use case? Python for training, java for deployment? Beyond that, you could also consider building your own wrapper maybe? In clojure, there's jutsu.ai for example: https://github.com/hswick/jutsu.ai

Disclaimer: I run a competing library to the uncomplicate series of libraries. If you want that set of guarantees with just as ease of use,you can look in to ours: http://nd4j.org/ We have various usage examples here: https://github.com/deeplearning4j/dl4j-examples/tree/master/... and the userguide: http://nd4j.org/userguide We have a lot going on underneath the hood, but we pre compile everything out of the box. We maintain a whole software stack for JNI called javacpp: https://github.com/bytedeco/javacpp One of those includes something called a "preset" which bundles openblas as a maven dependency (with the ability to link to mkl if you want) We support cuda as well. If you have any questions, stop by our live chat: https://gitter.im/deeplearning4j/deeplearning4j

Hard to tell from an HN user name :D. That's great to hear! I get how hard it can be - what you get out of it is learning though. We have some seriously cool examples that are just weekend projects for folks right now with javafx for example: https://github.com/deeplearning4j/dl4j-examples/pull/421 A lot of community contributions are in the examples now, if you haven't used dl4j in a while maybe take a look.

Yeah, I probably plug DL4J too much. It's a hard habit to break, and I'm sure some people get tired of it. I try to only do it when I have something substantive to say about the post in question. OpenAI and DL4J are equivalent in the sense that they both have active communities focused on parts of machine learning. The difference, of course, is that DL4J is a commercially backed OSS framework, while OpenAI is a not-for-profit think tank that is using other group's DL libs, while building its own sandbox and obviously conducting its own research. I wouldn't say that DL4J is more serious because Java is enterprise. It's just tackling different problems. It does some things better (many integrations) and some things worse or not at all (automatic differentiation), which is simply a reflection of our priorities. Not research, just production