Deep neural network training is a highly non-convex optimisation problem with poorly understood properties. We know that a solution can be found by following the negative gradient to walk down the loss landscape, but we have little guarantees that the discovered minimum will, for example, generalise well to unseen data. The shape of the loss landscape is important when choosing the best strategy to find a minimum, and may also be important when differentiating between two solutions equivalent in terms of the training error, but markedly different when performing inference on unseen data. Thus, quantifying and visualising neural network loss landscapes has much practical relevance. This talk aims to review the various methods used for loss landscape analysis, and to highlight some of the practical insights that this lens offers.