What makes deep learning “deep” is the stacking of at least 5-10 node layers, with advanced object recognition using upwards of 150 layers. Object recognition, as used by self-driving vehicles to recognize objects such as pedestrians and other vehicles, is a popular application of deep learning today. Other common applications of deep learning include time series analysis to analyze data trends measured over particular time periods or intervals, speech recognition, and text processing tasks including sentiment analysis, topic segmentation, and named entity recognition.
