Large volumes of passive acoustic data are collected by researchers, governments, and conservationists across the globe to monitor species for population assessments and conservation objectives. An analysis bottleneck often exists due to the lack of tools to process large datasets and identify detected signals to the species level. Deep learning has been shown to enable fast and effective detection of species-specific vocalizations. This study developed a Risso's dolphin (Grampus griseus) binary echolocation click classifier using a Long Short-Term Memory neural network. This classifier was trained and validated on 872 visually verified single species encounters collected during towed hydrophone surveys, primarily in the Northeast Atlantic. The classifier was subsequently tested on an independent set of 31 h of visually verified data collected from static and drifting recorders in Scottish and English waters, comprising four different delphinid species (including Risso's dolphins) as well as various sources of anthropogenic noise. The best classifier achieved an F1 score of 0.98 (precision 0.96, recall 0.98). While care must be taken when applying deep learning classifiers to new datasets, the performance of this model on independent data highlights its ability to generalize well within the study region, even when using data collected on different recording platforms.