This centers the columns of the image matrix by subtracting the mean and reduces the variance of the data by dividing by standard deviation. This class computes the mean and standard deviation of the all pixels in the image and subtracts the mean from each pixel and divides the pixels by the standard deviation.

In this article I describe how to achieve such effect. Here is the initial result.

Course material for graduate class ift6266h13. Leave a Reply Cancel reply.

Research Journal of Yangyang Zhao for Machine Learning Course. Second attempt with unsupervised pretraining. For the first layer, I try 2304 units gRBM, 576 units gRBM ,. 2304 units DAE, 576 units DAE. For the second layer, I use 576 hidden units gRBM. The minimum construction error is 195. 2304 hidden units gRBM layer fo.

My blog on the Representation Learning Course. Fine tuning of unsupervised pretraining. Here are the results for the model with GCN preprocessing. In both experiments I had to set the learning rate to 1e-5. The training algorithm diverged using higher values of the learning rates . As the results demonstrate, sigmoid units work better with GCN. In the next attempt, I used unsupe.