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Hello, fellow data enthusiasts! Today, we’re going to embark on an exciting journey through the world of hyperparameters in machine learning. If you’re like me, you probably love the thrill of building a machine learning model and watching it learn and improve. But have you ever wondered how we can make our models even better? The secret sauce lies in the science of hyperparameter tuning.
The implementation is available below:
Table of Contents
Introduction
Getting KerasTuner Up and Running
Using KerasTuner on a Classification Problem
Pro Tips for Mastering KerasTuner
Introduction
Hyperparameter tuning is a crucial step in building effective machine learning models. Hyperparameters are parameters whose values are set before the learning process begins, unlike model parameters which are learned during training. Examples include learning rate and number of layers in a neural network.
