Transfer learning enables you to use pretrained deep neural networks (e.g., AlexNet, ResNet, and Inception V3) and adapt them for custom image classification tasks. Danielle Dean and Wee Hyong Tok walk you through the basics of transfer learning and demonstrate how you can use the technique to bootstrap the building of custom image classifiers.

Yufeng Guo walks you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng takes you from a conceptual overview all the way to building complex classifiers and explains how you can apply deep learning to complex problems in science and industry.

Anna Roth and Cristian Canton walk you through building a system to recognize emotions by inferring them from facial expressions. Cristian and Anna explain how they trained their emotion recognition CNN from noisy data and how to approach labeling subjective data like emotion with crowdsourcing before showing a demo of this work in action, as it is exposed in Microsofts Emotion API.

Urs Muller presents the architecture and training methods used to build an autonomous road-following system. A key aspect of the approach is eliminating the need for hand-programmed rules and procedures such as finding lane markings, guardrails, or other cars, thereby avoiding the creation of a large number of if, then, else statements.