Major technological advances in cryo-electron microscopy (cryo-EM) have produced new opportunities to study the structure and dynamics of proteins and other biomolecular complexes. However, this structural heterogeneity complicates the algorithmic task of 3D reconstruction from the collected dataset of 2D cryo-EM images. In this seminar, I will overview cryoDRGN, an algorithm that leverages the representation power of deep neural networks to reconstruct continuous distributions of 3D density maps. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of 3D volumes and a learning algorithm to optimize this representation from unlabeled 2D cryo-EM images. Extended to real datasets and released as an open-source tool, cryoDRGN has been used to discover new protein structures and visualize continuous trajectories of their motion. I will discuss various extensions of the method for broadening the scope of cryo-EM to new classes of dynamic protein complexes and analyzing the learned generative model. CryoDRGN is open-source software freely available at http://cryodrgn.cs.princeton.edu..