In normal neural network, in the first layer we have data, random weights and bias term. This then pass through hidden layer, then out output layer, we then calculate the error, and finally based on error we again update the weights.
This looping is done continuously till we get satisfactory error rate.
But in CNN, we first use different filters (say 5 filters) to extract features. After 2 convulation layer and max-pooling plus padding plus flattening, we get a vector. This vector is input vector as we observe in normal NN. We also have random weights and bias terms. This value is then propagated into fully connected layer and follows the same pattern as we observe in normal NN.
My questions are the following:
1) In case of normal NN, the weights and biases are updated during backward propagation. In CNN, apart from weights and biases does filters values are also updated?
2) Related to first question, how the filters are chosen? "Filters are chosen" means types of filters are chosen. As per my understanding different filters extract different features of an image. As well as how number of filters are chosen.