In this article, we will take deploy a Super Resolution App on the cloud using lightning.ai. The primary goal here is to see how easy it is to create and…
In this post, we will build a Lightning App. Why? Because it is 2022, and it is time to explore a more modern take on interacting with, presenting, and…
The PyTorch team recently announced TorchData, a prototype library focused on implementing composable and reusable data loading utilities for PyTorch. In…
Today, PyTorch officially introduced GPU support for Apple’s ARM M1 chips. This is an exciting day for Mac users out there, so I spent a few minutes trying…
Developing good predictive models hinges upon accurate performance evaluation and comparisons. However, when evaluating machine learning models, we…
The cross-entropy loss is our go-to loss for training deep learning-based classifiers. In this article, I am giving you a quick tour of how we usually…
TorchMetrics is a really nice and convenient library that lets us compute the performance of models in an iterative fashion. It’s designed with PyTorch (and…
Machine Learning with PyTorch and Scikit-Learn has been a long time in the making, and I am excited to finally get to talk about the release of my new book…
One of my ML research mentees is following OpenAI’s Spinning up in RL tutorials (thanks to the nice folks who put that guide together!). She emailed me some good questions about the basics of Reinforcement Learning, and I wanted to share some of m…
By: Stefanie Jegelka and Ameet Talwalkar (ICML21 Communication Chairs) With ICML 2021 underway, we wanted to briefly highlight the upcoming invited talks. A general theme of the invited talks this year is “machine learning for science.” The Program Chairs (Marina Meila and Tong Zhang) have invited world-renowned scientists from various disciplines to discuss their problems …
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