layout: post
category: Tutorial
tags: derivative gradient
---
The tutorial in the linked [Jupyter notebook](https://github.com/ResourceHog/MLS-Training-Sessions/blob/master/NN/Session_1/Derivative_Gradient_Review.ipynb){:target="_blank"} walks through the basics of the derivative function. It builds on that knowledge to implement hill descent for optimizing a single parameter model and gradient descent for optimizing a multidimensional model. Matplotlib plots bring the explanations to life and modifiable code makes for a hands-on learning experience. Below are some screenshots to give you an idea of the content.

![Derivative](/img/deriv_screenshot.png){:width="100%"}

![Hill Descent](/img/hill_descent_screenshot.png){:width="100%"}