Our goal in this chapter is to describe the recent application of deep learning and artificial intelligence (AI) techniques to retinal image analysis. Automatic retinal image analysis (ARIA) is a complex task that has significant applications for diagnostic purposes for a host of retinal, neurological, and vascular diseases. A number of approaches for the automatic analysis of the retinal images have been studied for the past two decades but the recent success of deep learning (DL) for a range of computer vision and image analysis tasks has now permeated medical imaging and ARIA. Since 2016, major improvements were reported using DL discriminative methods (deep convolutional neural networks or autoencoder convolutional networks), and generative methods, in combination with other image analysis methods, that have demonstrated the ability of algorithms to perform on par with ophthalmologists and retinal specialists, for tasks such as automated classification, diagnostics, and segmentation. We review these recent developments in this chapter.