Tag Archives: Machine Learning

In this post, I will deal with back propagation, gradient descent etc. Do check out my previous posts regarding Max Pooling, filters, dropout layers, fully connected layers and CNN. To begin click here.SoWhat is Back Propagation?Backpropagation is done whenever we find some error. After obtaining the probabilities of the images the CNN subtracts the actual answerContinueContinue reading “Convolutional Neural Networks IV”

In this post, I’ll deal with dense layer, fully connected layer and backpropagation. If you have missed my previous post click here.Before moving further, let us have a view on the filter’s working on an image. I made a pixelated image of the letter ‘R’ and applied a 3 X 3 filter one time and 3ContinueContinue reading “Convolutional Neural Networks III”

This is my second post in CNN regarding max pooling, strides and padding.In the previous post we extracted the features from the image of ‘3’. Although the dimensions of the image were 4 X 24 which is quite small. But what to do when the size of the image is big having a very high resolution. GreaterContinueContinue reading “Convolutional Neural Networks II”

Every time I imagine CNN something spills out from my brain and forces me to restart my learning. I guess it was because I wasn’t doing practicals on CNN. Many guys basically look on CNN as a theory and that is where even I lost my way of learning. However, Coursera, Edx, and Udacity helpedContinueContinue reading “Convolutional Neural Networks I”