Multi Task Learning with W&B

In this post, I’ll walk you through my project "Faceless”. Some of the ideas are inspired from this article, Formulate your problem as an ML problem. We’ll apply these best practices around formulating your problem and will extensively cover multi-output classification. Weights & Biases was super useful in iterating through model architectures quickly and finding a good architecture for this project, and also in monitoring model performance. You can find the code for the project here, and the W&B dashboard with the metrics here.

​📈 Read the article here.

​👀 Check out the GitHub repo here.