> For the complete documentation index, see [llms.txt](https://ayushthakur.gitbook.io/ayush-thakur/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ayushthakur.gitbook.io/ayush-thakur/authoring/multi-task-learning-with-w-and-b.md).

# 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](https://developers.google.com/machine-learning/problem-framing/formulate). 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](https://github.com/ayulockin/faceattributes), and the W\&B dashboard with the metrics [here](https://app.wandb.ai/ayush-thakur/multi-output-classifier?workspace=).

## ​📈 Read the article [here](https://www.wandb.com/articles/multitask-learning-with-weights-biases-2). <a href="#read-the-article-here" id="read-the-article-here"></a>

## ​👀 Check out the GitHub repo [here](https://github.com/ayulockin/faceattributes). <a href="#check-out-the-github-repo-here" id="check-out-the-github-repo-here"></a>


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