what is deep learningmapping via deep sequence of simple data transformations (layersand that these data transformations are learned by exposure to examples now let' look at how this learning happensconcretely the specification of what layer does to its input data is stored in the layer' weightswhich in essence are bunch of numbers in technical termswe' say that the transformation implemented by layer is parameterized by its weights (see figure (weights are also sometimes called the parameters of layer in this contextlearning means finding set of values for the weights of all layers in networksuch that the network will correctly map example inputs to their associated targets but here' the thinga deep neural network can contain tens of millions of parameters finding the correct value for all of them may seem like daunting taskespecially given that modifying the value of one parameter will affect the behavior of all the othersinput goalfinding the right values for these weights weights layer (data transformationweights layer (data transformationfigure neural net work is paramet erized by it weight predictions yto control somethingfirst you need to be able to observe it to control the output of neural networkyou need to be able to measure how far this output is from what you expected this is the job of the loss function of the networkalso called the objective function the loss function takes the predictions of the network and the true target (what you wanted the network to outputand computes distance scorecapturing how well the network has done on this specific example (see figure input weights layer (data transformationweights layer (data transformationpredictions ytrue targets loss function loss score figure loss function measures he quality of he network' out put licensed to