This chapter explores the integration of deep learning and polymer data representations to advance polymer discovery. Polymers, widely used across industries, are represented as sequences, graphs, or vectors, each requiring tailored neural network frameworks. Fundamental tasks, including polymer property prediction and inverse design, are addressed through neural networks such as RNNs, GNNs, and MLPs, with advanced architectures leveraging self-attention mechanisms and Transformers for sequence and graph data.