Rochester SPS Neural Networks From Scratch Lecture 1: Regression and Gradient Descent

Neural networks are the dominant class of machine learning algorithms for computer vision and natural language processing today, and their rise in the past 10 years feels like a revolution. But the history of neural networks is not one of revolution, but evolution. Modern neural networks represent decades of fine-tuning of old ideas. In this first lecture, we discuss linear regression and logistic regression. These linear models, in addition to being useful in their own right, are examples that we can use to explain concepts critical to neural networks such as loss functions, gradient descent, and activation functions.

Speaker(s): Dr. Miguel Dominguez,

Virtual: https://events.vtools.ieee.org/m/275752