1. A method comprising: using a server computer, storing a plurality of training datasets, each of which comprising a plurality of training input matrices and a plurality of corresponding outputs; using the server computer, initiating training of a deep neural network using the plurality of training input matrices, a weight matrix, and the plurality of corresponding outputs; while performing the training of the deep neural network, identifying one or more weight values of the weight matrix for removal; removing the one or more weight values from the weight matrix to generate a reduced weight matrix; identifying, in the reduced weight matrix, a first weight value and a second weight value; identifying a plurality of values between the first weight value and the second weight value; assigning different integer values to each of the first weight value, the second weight value, and the plurality of values; generating a quantized weight matrix by performing, for each weight value of the reduced weight matrix; identifying a particular value of the first weight value, the second weight value, and the plurality of values that is closest in magnitude to the weight value; identifying a particular integer assigned to the particular value; replacing the weight value with the particular integer; storing the reduced weight matrix with the deep neural network.