Deep neural networks (DNNs) are powering the revolution in machine learning that is driving autonomous vehicles, and many other real-time data analysis tasks. The two most popular DNNs are convolutional — for feature recognition — and recurrent — for time series analysis.
DNNs need to be trained on massive tagged datasets to develop a model – basically a matrix of feature weights – that can then be run on local hardware. When a trained neural network classifies or estimates various values, the process is called inference.