In this talk I will walk the users through the entire process of building a convolutional neural network for image classification. The process starts with a flask application to label your data, followed by characterizing, training, and evaluating the CNN using Pandas, Jupyter Notebooks, and Bokeh plots. Finally we show how the CNN can be deployed and used in real-world applications. Abstract Convolutional Neural Networks: they’re new, they’re big, they’re complex, they’re poorly documented and accordingly they are a little scary. At Planet we will image the entire earth every day, and to deliver that data to customers we need to analyze images without it ever being seen by human eyes. In this talk we’ll cover how to build, train, and characterize a neural net for image classification all from the comfort and safety of a Jupyter notebook. This talk will serve as a template for building and using your very own CNN.
Bio Katherine Scott is a senior software engineer at Planet working on image classification. Prior to planet Ms. Scott was the co-founder and CTO of Tempo Automation and a co-founder at Sight Machine.