Interested in how we think and how machines learn?
This is a group to discuss both natural and artificial intelligence: how they differ, how they are alike, what they can learn from each other, how we can learn to harness them, and why it is important to study them.
We are always open to those who find intelligence fascinating!
In this workshop you'll learn the fundamentals of PyTorch using an incremental, from-first-principles approach. We'll start with tensors, autograd, and the dynamic computation graph, and then move on to developing and training a simple model using PyTorch's model classes, datasets, data loaders, optimizers, and more. You should be comfortable using Python, Jupyter notebooks, Google Colab, Numpy and, preferably, object oriented programming.
Notebooks have become the worldwide standard environment for data scientists to work in. It is very easy to get started, set up experiments and build models. It is difficult however to go from an exploratory notebook to a fully operationalized model. In this tutorial we will look at how to structure your workflow such that the same logic can easily be reproduced, the code be packaged and turned into a system that the business can rely on.
The efficacy of images to create quantitative measures of urban perception has been explored in psychology, social science, urban planning and architecture over the last 50 years. The ability to scale these measurements has become possible only in the last decade due to increased urban surveillance in the form of street view and satellite imagery, and the accessibility of such data.
This talk will present a series of projects which make use of imagery and CNNs to predict, measure and interpret the social and physical environments of our cities.