This chapter provides an overview of deep learning (DL), its history, and key innovations that contributed to its success. It reviews diverse approaches of learning from data, along with common DL methods and architectures used in ophthalmic applications, such as image enhancement, segmentation, and disease classification, where DL models have achieved remarkable accuracy, even surpassing human performance have been reviewed. Beginning with the basic feedforward neural network architecture, the chapter progressively introduces more advanced methods, including convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks. Recent advancements, including language models and their applications in ophthalmology, are also discussed. The chapter emphasizes how DL has transformed ophthalmic imaging and diagnosis by leveraging various data-driven approaches.