pushpull13/Gradient-Descent-Scratch

It is a gradient descent algorithm for classification implemented from scratch using numpy library. It is good practice to shuffle data at first numpy.random.shuffle() Mini Batch Size is size of input data flowing through network at a time for calculating error as a whole Learning Rate Alpha decides the rate at which, weights and biases will update while back propagation Number of Epochs decides number of times, the whole dataset will be used to train the network Set Mini Batch Size to 1/10th of total data available. And update it manually after every train of network to find its optimum value Alpha should be selected such that learning isn't very slow as well as it didn't take long jump or else, network will start diverging from local minima Number of epochs are selected such that network don't overfit itself over noise In ANN, output will depend on every neuron it pass through For output layer, we have label according to which, it is possible to find it's expected value But for all other layers, there is no single solution available So, finding optimum value is little harder for that