Do you know a dataset for regression where deep learning outperforms svm and random forests?
As a general rule, convolutional deep networks will perform better than SVM and Random Forest models on data that has hierarchical spatial/temporal structure, so for example sound, images, video.  Deep networks are very good at learning this spatial/temporal structure at different levels of granularity.  For classic datasets like Iris uses for teaching ML, however, I seriously doubt deep networks would perform better than SVM or Random Forest.