313. Advanced Neural Network Methods
Take a neural network from bare-bones to state-of-the-artWatch Promo
Welcome to Advanced Neural Network methods. You can enroll below for free or, if it's easier, unlock the entire End-to-End Machine Learning Course Catalog for 20 USD.
The course is based on the Cottonwood machine learning framework and demonstrates how to freely experiment with deep learning concepts of your own.
Thank you and for your enthusiasm and dedication working through the courses! Your experiences give me great ideas for how to make the material even more helpful and motivation to keep it coming at a good pace. I couldn’t do it without you.
Preview1.0 How Regularization Works
Preview1.1 Constructing a Network with Regularization (7:14)
Preview1.2 Executing Regularization during Stochastic Gradient Descent (3:35)
Preview1.3 L1 Regularization (LASSO) (4:45)
Preview1.4 L2 Regularization (Ridge or Tikhonov) (2:15)
Preview1.5 Regularization in Action (2:55)
Preview1..6 Custom Regularizers (3:25)
I love solving puzzles and building things. Machine learning lets me do both. I got started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook I worked to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery and to do a better job identifying topics reliably in unstructured text. Now at iRobot I work to help robots get better and better at doing their jobs. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.