In this unit, after reviewing where we’ve been, we push towards state-of-the-art models (still focusing on computer vision). We’ll first show how our work last 2 weeks connects to the pre-trained models we used in the opening weeks. Then, we’ll introduce or revisit tools that allow our models to achieve high performance, such as data augmentation and regularization.
Generalization
Students who complete this unit will demonstrate that they can:
Describe how to use neural models to implement text classifiers and recommendation systems
Explain the importance of evaluating image classifiers on unseen data.
Identify some examples of data augmentation and regularization.
Predict the effect of data augmentation and regularization on model training.