  This doctoral thesis covers some of my advances in electron microscopy with
deep learning. Highlights include a comprehensive review of deep learning in
electron microscopy; large new electron microscopy datasets for machine
learning, dataset search engines based on variational autoencoders, and
automatic data clustering by t-distributed stochastic neighbour embedding;
adaptive learning rate clipping to stabilize learning; generative adversarial
networks for compressed sensing with spiral, uniformly spaced and other fixed
sparse scan paths; recurrent neural networks trained to piecewise adapt sparse
scan paths to specimens by reinforcement learning; improving signal-to-noise;
and conditional generative adversarial networks for exit wavefunction
reconstruction from single transmission electron micrographs. This thesis adds
to my publications by presenting their relationships, reflections, and holistic
conclusions. This version of my thesis is typeset for online dissemination to
improve readability, whereas the thesis submitted to the University of Warwick
in support of my application for the degree of Doctor of Philosophy in Physics
is typeset for physical printing and binding.
