xmlutil VS kotlindl

It does apper to be a bug in serialization rather than xmlutil itself as the dev confirms here. Either way, at the end of the day a dependency that sporadically fails the compilation with no reason and requires disabling incremental compilation is not acceptable for production use and I've settled to use StAX instead.

It now supports JSON, ProtoBuf and CBOR. XML is a community-supported format that is provided by xmlutil

This is a really nice sample app (works out of the box) that Jetbrains have made to showcase the capabilities of this Kotlin Deep Learning library https://github.com/Kotlin/kotlindl

There are Java bindings for TensorFlow, but that's quite low level. I tried to see if I can get some Keras API for Scala, but I'm no expert and haven't had enough time to invest in this, so it's stuck in alpha. Maybe I develop it slow burning over the next year. A bit envious that Kotlin has a Keras-like library.

KotlinDL 0.3 is available now on Maven Central with a variety of new features — check out all the changes that are coming to the new release!

Could you please add an issue with your use-case and proposed solution? Or write here more details. Do you need just serialization to objects in memory? or just in bytes?

While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features.

For deep learning this is your best bet: https://github.com/jetbrains/kotlindl

So, the KotlinDL library could solve classification and regression tasks on tabular data and image data.

Thank you for your feedback! Kotlin∇ is a research prototype for building differentiable programs from basic primitives. If you’re looking for a higher-level API, KotlinDL [1] or KInference [2] might be a better fit for building end-user applications such as image, speech or language processing.

[1]: https://github.com/JetBrains/KotlinDL

[2]: https://github.com/JetBrains-Research/kinference/