The first thing that must be discussed in the matter, is the mathematics involved.
Deep learning is first and foremost a mathematical model. An overview of this big subject is given quite well in its Wikipedia article. The page showcases a summary of the subject, as well as links to more resources (2 birds in 1 go).
To explain the mathematical model, I recommend reading this article. This gives a relatively good basis of understanding for neural networks (which are a "supertype" of deep learning algorithm). Afterwards, page 4 of the article is a good place from which to progress towards Deep learning. For example, the neural network in the image from that page:
But what if we had more than 1 hidden layer? Now that's classified as deep learning.
A short google search for "deep learning neural networks" gives this handy website. It, too, gives a brief summary of the mathematics, increasing understanding.
After reading these articles and that website, you and your students should be good to go, and start gaining hands-on experience. Usually, the basic example for a deep learning algorithm is to fit a linear function to a dataset of points.
Another article (again, found through google search) explains the subject with more specific information. I recommend reading it to gain greater understand of the subject.
Because links sometimes go broken, it might be a good idea to download those articles (they are pdf files). Then again, they were found with straight forward google searches, so that's more reliable, especially in such a quickly evolving field.