I was fortunate to take this class the first time it was offered. I found it a great introduction to the material, but a bit over my head. Deep learning requires a strong grasp of linear algebra - and particularly at the "Stanford" level. My undergrad didn't prepare me well for visualizing outer products and matrix / tensor derivatives. Once you get over those hurdles, deep learning is quite fun. It often works like magic. I'll give you an example:
A firetruck is _____
Try typing this in Google and you'll get "red", "moving" and "made". During the course you build a network that trains next-word completions using arbitrary bodies of text. You can train it for hours, days or weeks...and it just gets better and better. Eventually you will max out the capacity of your network, but then you can fiddle with the number of nodes and other hyperparameters. In the end you're just training a "black-box" nonlinear function to best approximate an unknown function defined by training data.
A firetruck is _____
Try typing this in Google and you'll get "red", "moving" and "made". During the course you build a network that trains next-word completions using arbitrary bodies of text. You can train it for hours, days or weeks...and it just gets better and better. Eventually you will max out the capacity of your network, but then you can fiddle with the number of nodes and other hyperparameters. In the end you're just training a "black-box" nonlinear function to best approximate an unknown function defined by training data.