where x is the input data, f (•; θ) is the deep neural network with learnable parameters θ, y is the output prediction, ŷ is the ground truth, and ← is the learning algorithm or training strategy that forces y to approach ŷ. By effectively formulating input data, defining output tasks, designing network modules and architectures, and developing learning strategies, it is possible for deep learning to precisely understand and generate microscopic particles.