Mathematics: gradient descent, and describing data honestly
Two ideas carry most of the mathematics you need. The first is gradient descent: step downhill on an error surface until you reach a low point. Change the learning rate and watch it crawl, converge, or bounce out entirely — that one control explains a large share of training failures. The second is describing a distribution honestly: mean, median, spread and shape, and why the mean stops being a useful summary the moment the data is skewed. You do not need to derive anything here. You need to see what the numbers do when you move them, so the formulas you meet later describe something you have already watched happen.
Calculus
Gradient descent
Training is just rolling downhill. At each step the model moves against the slope of the loss. The learning rate sets the step size — too big and it overshoots, too small and it crawls.
A moderate learning rate slides smoothly to the minimum.
Probability & statistics
Describe your data
Click anywhere on the line to drop a data point. Mean, median, mode, and standard deviation update instantly — see how a few outliers pull the mean away from the median.
When mean and median drift apart, your data is skewed by outliers.