Gradients

Vectors, dot product, matrices, matrix-vector product, and gradients — with RL motivation and practice.

Derivatives, chain rule, and partial derivatives — with RL motivation and practice.

Vectors, dot product, matrix-vector product, and gradients — with RL motivation and explained solutions.

The chain rule applied backwards through a neural network — computing gradients for every weight and verifying them with numerical finite differences.