- Introduction to the Jupyter Notebook
- Functions and Their Computational Graphs
- Formalizing The Problem
- What is a derivative? A gradient?
- Gradient Descent and Backpropagation
- Writing an Image Recognition Program in Python
This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus. Although the prerequisite is minimal, we will discuss many advanced topics including:
1) functions and their computational graphs.
2) neural networks
3) conceptually understand the derivative and the gradient.
4) gradient descent and backpropagation
5) the multivariable chain rule
6) mini-batch gradient descent
I am currently a faculty at Boston Latin School and a lecturer at the University of Massachusetts Boston(Umass Boston). I received both my Masters and Ph.D. in Mathematics at Brigham Young University. I am passionate about teaching and my interest is to bring interesting ideas in math and computer science accessible to a wide audience.