Understanding and Applying Deep Learning

Abstract

The past 10 years have witnessed an explosion in deep learning neural network model development. The most common perceptual models with vision, speech, and text inputs are not general-purpose AI systems but tools. They automatically extract clues from inputs and compute probabilities of class labels. Successful applications require representative training data, an understanding of the limitations and capabilities of deep learning, and careful attention to a complex development process. The goal of this view is to foster an intuitive understanding of convolutional network deep learning models and how to use them with the goal of engaging a wider creative community. A focus is to make it possible for experts in areas such as health, education, poverty, and agriculture to understand the process of deep learning model development so they can help transition effective solutions to practice.