Recap
Let's have a quick overview of what you have learned and test it on the given notebook.
We'll cover the following...
General overview
This finishes our journey through the inner workings of gradient descent. By now, we hope you are able to develop better intuition about the many different aspects involved in the process.
In time and with practice, you will observe the behaviors described here in your own models. Make sure to try plenty of different combinations: mini-batch sizes, learning rates, etc. This way, not only your models will learn but so will you!
This is a (not so) short recap of everything we covered in this chapter:
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Defining a simple linear regression model.
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Generating synthetic data for it.
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Performing a train-validation split on our dataset.
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Randomly initializing the parameters of our ...