Hardware implementation is critical to reducing execution time and energy consumption for the training and deployment of deep learning models. The use of field-programmable gate arrays (FPGAs) is a promising approach to achieve a good trade-off between the design cycle and performance for deep learning systems. This lecture on FPGA-based deep learning consists of two parts. The first part gives an overview of state-of-the-art FPGA design for training and inference of deep learning models. Specifically, this part covers potential benefits, application scenarios, main challenges, design optimisation techniques for FPGA-based deep learning with examples. The second part discusses a basic FPGA design for feed-forward networks (FFNs). The design accelerates the back-propagation process for FFN training and can be extended to support more complicated network architectures.