My blog has moved to jwuphysics.substack.com. You can also see [my home page](https://jwuphysics.github.io/) for more content.

Posts

  • Visualizing deep learning with galaxies, part 2

    In the previous post, we examined the feature space of galaxy morphological features. Now, we will use the Grad-CAM algorithm to visualize the parts of a galaxy image that are most strongly associated with certain classifications. This will allows us to identify exactly which morphological features are correlated with low- and high-metallicity predictions.

    Aug 27, 2020

  • Visualizing deep learning with galaxies, part 1

    Let's explore the morphological feature space of galaxies represented by a trained CNN. We will use PCA to reduce the dimensionality of the neural network's latent features, and then visualize these features with matplotlib.

    Jul 27, 2020

  • Identifying racial bias in health data

    We are becoming more and more reliant on machine learning algorithms in our everyday lives. But what if these algorithms aren't fair? In this exploratory data analysis of Obermeyer et al. 2019, we look at how racial biases can creep in to an algorithm's decision-making process.

    Jun 11, 2020

  • Training a deep CNN to learn about galaxies in 15 minutes

    Let's train a deep neural network from scratch! In this post, I provide a demonstration of how to optimize a model in order to predict galaxy metallicities using images, and I discuss some tricks for speeding up training and obtaining better results.

    May 26, 2020

  • Deep learning with galaxy images

    Welcome! In this first post to my blog, we will take a deeper look at galaxy images. Why should we bother measuring the metallicities, or elemental abundances, of other galaxies? And why would we use convolutional neural networks? Read more to find out!

    May 21, 2020