Over the next couple of weeks, we'll look at neural nets & deep learning : discussion , readings, examples.
To understand this completely means wading through a lot of math.
Some of the tutorials will instead focus on how to use code libraries and tools - what the inputs are, how to call their API, and what you get. You'll need some intuition to do that, even without mastering the math.
... and our "scratch" textbook, chapters 18 & 19.
My plan is to talk through the guru99 notes and the chap 18 textbook material on Friday, and then ask you to look at this yourself over the weekend. I suggest answering the following questions, running his code, and making some plots to see what is converging to what.
Here's what you should try to understand :
In the simple linear regression setup, the model was y=a*x+b. The target (goal) was to predict y, the inputs (features) was x, and the model parameters were (a,b). During "learning", we found a way to start at some (a0,b0) guess, and then change (a,b) until the least squares error was a minimum.
So, now we have a new model : neural nets. Pick an example from the readings.
