Working on now: Learning more about how machine learning models are evaluated beyond just loss/accuracy/confusion matrices, like how things like generalization are tested and the impact different testing methods have (like 10-fold vs 1-fold cross validation).
Learned last: I learned how to make 1D convolutional autoencoders with keras. I also learned that autoencoders may not be a good choice for my dataset.
Working on now: Learning more about how machine learning models are evaluated beyond just loss/accuracy/confusion matrices, like how things like generalization are tested and the impact different testing methods have (like 10-fold vs 1-fold cross validation).
Learned last: I learned how to make 1D convolutional autoencoders with keras. I also learned that autoencoders may not be a good choice for my dataset.