Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well.

Why was AlphaGo's Move 37 against Lee Sedol so significant? Why was it so important that I named my 10 week course on deep reinforcement learning on it? In this final video of my course, I'll explain what move 37 symbolized for humanity and detail 3 examples of how it will affect healthcare, design, and decision-making. We'll go through a code example of a Generative Adversarial Network and even discuss China ambitious 2030 AI initiative. Theres a lot that I cover in this video, I hope that it helps connect the dots.

Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique.