If you're just getting into ML/AI and want to work with Kotlin + computer vision, a great way to really
understand things is to build them yourself. One of my favorite ideas is based on Richard Feynman’s approach:
“What I cannot create, I do not understand.”
Re-implementing core pieces of an ML framework— even small ones — is an amazing learning path. It might sound like a big undertaking, but since you're already here in the Kotlin community, it probably means you're interested in how things
actually work under the hood. That aligns really well with the kind of work we’re doing with
SKaiNET.
If you enjoy going deeper, try implementing the basics in Kotlin yourself: tensor ops, tiny autograd, a minimal convnet, etc. You’ll learn more from that than from any tutorial, and you might even find yourself contributing to existing Kotlin ML projects along the way.
Happy to help you get started if you want pointers — always great to have new people jump in!
Its still very early stages, but already having basic MPL on start and CNNs are just the current work in progress....