Nowadays, auto-encoders systems with pretrained RBMs are not used anymore? To me, a beginner in machine learning it sounded like a good idea to train a model to reproduce the original input, forcing the model to compress the original features from 33 to 4 and then back to 33. A FNN like you used does not do that, right? It just goes from…
Nowadays, auto-encoders systems with pretrained RBMs are not used anymore? To me, a beginner in machine learning it sounded like a good idea to train a model to reproduce the original input, forcing the model to compress the original features from 33 to 4 and then back to 33. A FNN like you used does not do that, right? It just goes from 33 to 4 and then you train it to predict 2 labels. The old technique is not more robust?
Nowadays, auto-encoders systems with pretrained RBMs are not used anymore? To me, a beginner in machine learning it sounded like a good idea to train a model to reproduce the original input, forcing the model to compress the original features from 33 to 4 and then back to 33. A FNN like you used does not do that, right? It just goes from 33 to 4 and then you train it to predict 2 labels. The old technique is not more robust?
Nowadays, we don't need to pretrain RBMs. The current technology now allows us to do everything in one pass, which is what I meant :)