This post is in a series on doing machine learning with unbalanced datasets. This post focuses on the evaluation aspect in particular. For background, please see the setup post.

This post is in a series on machine learning with unbalanced datasets. This post focuses on the makeup of the validation set in particular. For background, please see the setup post.

This post is in a series on machine learning with unbalanced datasets. This post focuses on the training aspect. For background, please see the setup post.

This post is the first in a series on working with unbalanced data. We’ll answer questions like how to train a model, how to validate it, and how to test it. Is it better than your datasets be balanced or representative of the real-world distribution?

As machine learning has continued to expand, so has the need for data. I’ve put together some of my favorite resources for finding datasets. I hope they are some service.

I find it difficult to keep up with the latest in machine learning, even though it’s part of my full-time job. Fortunately, there are a lot of resources out there to help sort through it all. I thought I would put together a list of resources that I use in case it helps anyone else. If you know of a great resource that I’m missing, please let me know!

This post is going to demonstrate how to use the Albumentations library with TensorFlow.