I'm Andrich van Wyk, a software architect and ML specialist. This is my personal blog; I write here about data science, machine and deep learning and software engineering. All opinions are my own.
Implementing the technique in Tensorflow 2 is straightforward. Start from a low learning rate, increase the learning rate and record the loss. Stop when a very high learning rate is reached. Plot the losses and learning rates choosing a learning rate where the loss is decreasing at a rapid rate.
An end-to-end example of how to create your own image dataset from scratch and train a ResNet50 convolutional neural network for image classification using the FastAI library.