Standing on the shoulders of giants, we don't have to design, create, and train a neural network, but instead, use one that already exists. We download a public domain fully trained neural network and modifying it slightly (to make it load faster during the session).
I'm teaching a junior college Python course and usually show this at the end of a 16-week intro class. I.e., you don't need to be a Python expert to get something out of this session. Knowing a little Python will be helpful, but all demos easily translate into other languages, like Java for instance.
After the session you will have a general understanding of "Neural Word Embeddings", understand what "cosine similarity" means and how to calculate it. I know, that may not sound all that exciting. But imagine you type in the question, "Men is to boys what woman is to" and your Python program answers with the word "girls". Or you type "Men is to king, what woman is to" and your Python program answers with the word "queen". But that is just the beginning, I will also show you an example that uses the same technique applied to a much more relevant topic, detecting bias in a text.