This is Part 2 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1 was all about the foundational concepts of machine learning. In this part I get into more advanced machine learning concepts. These include: [00:13] Optimization (I explain calculus!!!) [04:40] Gradient descent [06:26] Perceptron (or linear models – we learned what these are in part 1 but I expound a bit more) [07:04] Neural Networks (as an extension to linear models)[09:28] Brief Review of TensorFlowHope you enjoy Part 2! As always feel free to send any feedback or add any comments below if you have any questions. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) Deep Learning vs. Machine Learning Get Started with Machine Learning