# Replicating Scaling Laws by using MNIST data

I started following Deep Learning Curriculum written by Jacob Hilton and here is what I learnt from the exercise in Topic 2 - Scaling Laws. **My solution is written in Colab T2-ScalingLaws-solution.ipynb**

It took me around 15 hours to finish the exercise. Throughout the process I learnt:

- How to vary the CNN width and training data to follow scaling laws experimentation set up.
- How to use Pytorch lighting learning rate finder to adjust the learning rate based on model size.
- use
`callbacks.LearningRateFinder`

from pytorch lighting and do some experimentation to find the proper minimum and maximum learning rate to search from. Plot the learning rate to make sure the result looks right.

- use

- How the compute-efficient model size varies with compute.
- To approximate the relationship between compute and loss, we can use Cubic Root Function. We need to train more episodes to enable an accurate approximation.

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