Splash Biography

How do machines learn? In this mini-course, we'll cover the basics of deep learning (gradient descent, activation functions, neural networks) and then write some code to train our own neural network to do image classification. Given an image of an animal, we will train our neural network to output the category of the animal (e.g. if it's a bird, or a dog, or a cat, etc). (You can also use any other dataset you want). The class is not math-heavy at all. Everything will be explained conceptually and visually, assuming no prior background (though calculus will be a little helpful). We will be coding with Python and TensorFlow/PyTorch (the 2 most popular Python libraries for deep learning).

How do social networks like Twitter and Facebook function? Or Google Maps' route navigation? Underlying all these technologies are graphs, a useful data structure representing objects and the relationships between them. In this short "course", we'll explore graphs through hands-on coding, covering how to implement them and travel through them. As a fun application of graphs, we'll code our own version of the algorithm used by The Oracle of Bacon to find how 2 people are connected to each other through mutual friends/relationships. (You can play around with the Oracle of Bacon here: https://oracleofbacon.org) We'll also briefly touch on other problems in graph theory (like shortest path) and algorithms for solving them which build off what you will learn in this "course". Absolutely no prior knowledge of graphs is assumed. Get ready to learn about something cool and exciting! In addition, for those interested in competitive programming and/or software engineering careers, sometimes competitions and interviews have graph problems. This "course" would serve as a useful intro to graphs and point out useful algorithms in the field for further exploration.