Weights & Biases is excited to support the Ocean Cleanup data annotation challenge organized by Kili Technology in partnership with isahit and OVHcloud.

Building experiments doesn’t just end once the model is deployed. Teams need to monitor their models in production and use their findings to iterate further. Especially when dealing with tens to hundreds of models you need to monitor and automate at scale!

The W&B team will be at WAICF in Cannes, France April 14 - 16

What to do to spend more time on ML and less time on Ops

A Hugging Face course study group for fastai developers looking to leverage fastai to train and and deploy Transformers.

A Hugging Face course study group for fast.ai developers looking to leverage fast.ai to train and deploy Transformers.

How can we support effective, reproducible, and explainable deep learning and coordination across practitioners?

Over the next 20 weeks, we'll dive into this hands-on-guide to deep learning!

Greg talks about his recent paper "Feature Learning in Infinite-Width Neural Networks".

W&B community members talk about their experience reproducing top ML papers as part of Papers With Code's ML Reproducibility Challenge.

Javier gives an overview of the Loss Landscape project and shares some of his observations on neural network loss functions and high-dimensional objects in general.

Teams from UC Berkeley's ML@B student organization present their recent projects on gesture detection, capsule networks, and adversarial attacks.

Maithra presents on the differences between wide and deep networks.

Diganta talks about smooth activations, robustness, and catastrophic forgetting.

Towards Practical Multi-object Manipulation using Relational Reinforcement Learning, by Li, Jabri, Darrell, and Agrawal