  These brief lecture notes cover the basics of neural networks and deep
learning as well as their applications in the quantum domain, for physicists
without prior knowledge. In the first part, we describe training using
backpropagation, image classification, convolutional networks and autoencoders.
The second part is about advanced techniques like reinforcement learning (for
discovering control strategies), recurrent neural networks (for analyzing time
traces), and Boltzmann machines (for learning probability distributions). In
the third lecture, we discuss first recent applications to quantum physics,
with an emphasis on quantum information processing machines. Finally, the
fourth lecture is devoted to the promise of using quantum effects to accelerate
machine learning.
