It's a rare case when a neural net will give you materially different results than a random forest or an svm. ie, if an svm is 80% accurate, a well tuned neural

It's a rare case when a neural net will give you materially different results than a random forest or an svm. ie, if an svm is 80% accurate, a well tuned neural net might be 83% accurate. sometimes that's a huge difference. in a domain like medical image classification, maybe not so much also you can just "throw all the data" at an svm or random forest, or any number of similar models. Automated parameter tuning can be convenient, but it's prone to overfitting and doesn't eliminate a lot of the actual work