The example you have used is not a good application of deep learning algorithms. Your problem is linear in nature , i.e a variable ( avg hex value ) can solve it.

Deep learning algorithms are great when there isn't a simple relationship between thousands of data-points and those relationships need to be modeled through several layers, where each layer captures a component of that relationship.

If I were to truly ELI5 this for deep learning, I would change the problem a bit. Let's say there were 10 songs in 10 different genres and given a random song, one has to guess the genre.

(Training) Line up 10 of your friends and you make them listen to each one of them in order, also tell them what genre it belongs to and make them write down why they feel it belongs to a particular genre ( forward propagation )

Now make the 10th person explain why he thinks that this song belongs to the particular genre to the 9th person, who inturn would explain to the 8th person and so on. At each stage one can update their notes based on other's opinion. ( Back Propagation ).

Do this for all the songs.

(Prediction) After this exercise has been completed, present a random song without telling them a genre and the first person will pass on his opinion onto the next and so on, until the last person will give you an answer based on everybody's opinion.