#Machine Learning RoadMap for DL4J
This is a collection of progressively harder projects that I learned on and you can review or fix. Some are missing flawed etc When I set out to learn I didnt plan on writing this so its a very ugly collections. Its sequential in nature so it should be a good roadmap for someone else.
If you want to add something to this please don't hesitate to let me know. Im trying to touch on each broad category of machine learning.
Some of the more interesting things don't work or are a work in progress. Also let me know if you want to team up on a harder project or find bugs here.
If your a newb what to expect
https://github.com/eclipse/deeplearning4j-examples
done
thought these would be cool
done
not included I used the dl4j example repo
done
done
done with bug
done two contrived examples of simple stock trader design
done stock example extended
not included I used the dl4j example repo
done stocks example
done celeb a dataset
super important idea because it shows how networks can start learning simple things and advance to more complicated without direct intervention -- Ony makes modern art at the moment
separate git https://github.com/cagneymoreau/ReinforcmentLearning
Use the two different style of networks and view the activations maps or the filters maximizations
Use the Oxford-IIIT Pets dataset to perform segmentation and classification opening tar.gz too U-Net
vectorization and basic concept of dimensional tunneling vs static structure https://www.youtube.com/watch?v=XrZ_Y4koV5A https://github.com/treo/dl4j_attention
prado pqrnn realm quantization fp to int