- Introduction
- Functions and Their Computational Graphs
- Formalizing The Problem
THIS COURSE IS NOW FREE!!
Because of my busy schedule, I will not be able to maintain or support this course. Udemy requires that video content must be under 2 hours to make a course free. So I have unpublished most of the videos to satisfy the requirements. Please see my youtube channel for all lecture videos. Youtube Channel: @longnguyen8112
Enjoy!
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
THIS COURSE IS NOW FREE!!
Because of my busy schedule, I will not be able to maintain or support this course. Udemy requires that video content must be under 2 hours to make a course free. So I have unpublished most of the videos to satisfy the requirements. Please see my youtube channel for all lecture videos. Youtube Channel: @longnguyen8112
Enjoy!
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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.