Analysis of Dropout
Overfitting is a problem in Deep Neural Networks (DNN): the model learns to classify only the training set, adapting itself to the training examples instead of learning decision boundaries capable of classifying generic instances. Many solutions to the overfitting problem have been presented during these years; one of them have overwhelmed the others due to its simplicity and its empirical good results: Dropout.
Convolutional Autoencoders in Tensorflow
How to implement a Convolutional Autoencoder using Tensorflow and DTB.
Convolutional Autoencoders
The convolution operator allows filtering an input signal in order to extract some part of its content. Autoencoders in their traditional formulation do not take into account the fact that a signal can be seen as a sum of other signals. Convolutional Autoencoders, instead, use the convolution operator to exploit this observation. They learn to encode the input in a set of simple signals and then try to reconstruct the input from them.
Introduction to Autoencoders
Autoencoders are neural networks models whose aim is to reproduce their input: this is trivial if the network has no constraints, but if the network is constrained the learning process becomes more interesting.