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The two unsupervised learning tasks we will explore are clustering the data into groups by similarity and reducing dimensionality to compress the data while maintaining its structure and usefulness.

The goal of gradient descent is to find the minimum of our model’s loss function by iteratively getting a better and better approximation of it.