Building deep learning solutions in the real world is a process of constant experimentation and optimization.

Unlike any other type of software application, deep learning applications don’t have a linear graph lifecycle and relies on the fact that models need to constantly refined, optimized and tested.

In a nutshell, model optimization is directly proportional to model robustness!

Being a deep learning practitioner, you cannot deny the fact that choosing the correct hyperparameters for your model is a very critical and painful task.

So, Google’s TensorFlow created an awesome framework to solve the pain points of performing a hyperparameter tuning and optimization.

The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your real world Deep Learning applications.

In this article we will see, how we can use the Keras Tuner and TensorFlow 2.0 to choose the best hyperparameters for our model!

Before starting with the awesomeness of Keras Tuner, let’s warm up with some critical concepts to move smoothly with this blog.

#keras #artificial-intelligence #python #data-science #tensorflow