I recently just got into Machine Learning and decided to make my own Neural Net class (and also convolution neural net class, but that isn't quite done yet) in Java just to experiment with ML and see what I could do. For me, it was a fun opportunity to learn more about Neural nets and try to figure out how they work. As I was reading ML articles, it always felt like they glossed over how to do batch processing and it felt like even fewer articles described how to calculate the gradient in a batch learning model in a way that was understandable for someone who hasn't studied this area before. Since it seemed like there weren't a ton of concrete examples from my searches, I thought maybe I'd post this here in case anyone else was curious. I know I still have stuff to do on my project, but I'd appreciate any thoughts / constructive criticism on what could be done better / or ideas on what to do next!