Sunflower
Today I noticed that the sunflower that grew from a seed dropped by a bird was mostly open, so I took a photo of it. Because the strong winds recently bend it, Alice has attached it to the fence that's covered in ivy.
Effective testing for machine learning systems
Working as a core maintainer for PyTorch Lightning, I've grown a strong appreciation for the value of tests in software development. As I've been spinning up a new project at work, I've been spending a fair amount of time thinking about how we should test machine learning systems. A couple weeks ago, one of my coworkers sent me a fascinating paper on the topic which inspired me to dig in, collect my thoughts, and write this blog post.
Source: Effective testing for machine learning systems, an article by Jeremy Jordan.
Training PyTorch models with differential privacy
We are releasing Opacus, a new high-speed library for training PyTorch models with differential privacy (DP) that’s more scalable than existing state-of-the-art methods. Differential privacy is a mathematically rigorous framework for quantifying the anonymization of sensitive data. It’s often used in analytics, with growing interest in the machine learning (ML) community. With the release of Opacus, we hope to provide an easier path for researchers and engineers to adopt differential privacy in ML, as well as to accelerate DP research in the field.
Source: Introducing Opacus: A high-speed library for training PyTorch models with differential privacy, an article by Davide Testuggine and Ilya Mironov.