summary learning means finding combination of model parameters that minimizes loss function for given set of training data samples and their corresponding targets learning happens by drawing random batches of data samples and their targetsand computing the gradient of the network parameters with respect to the loss on the batch the network parameters are then moved bit (the magnitude of the move is defined by the learning ratein the opposite direction from the gradient the entire learning process is made possible by the fact that neural networks are chains of differentiable tensor operationsand thus it' possible to apply the chain rule of derivation to find the gradient function mapping the current parameters and current batch of data to gradient value two key concepts you'll see frequently in future are loss and optimizers these are the two things you need to define before you begin feeding data into network the loss is the quantity you'll attempt to minimize during trainingso it should represent measure of success for the task you're trying to solve the optimizer specifies the exact way in which the gradient of the loss will be used to update parametersfor instanceit could be the rmsprop optimizersgd with momentumand so on licensed to