Deep Learning, Regularisation, Optimization Algorithms, Deep Neural Networks, Gradient Descent.
as the distribution of the input changes, we might have to train the model again. Consider a deep neural network: And let’s only consider the learning of layer 3. It will have activations from layer two as its input: The aim of the third hidden layer is to take these activations and map them with