Hyperparameter Tuning with Keras Tuner

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In this Free Guided Project, you will:
2 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. At the time of recording this project, Keras Tuner has a few tuning algorithms including Random Search, Bayesian Optimization and HyperBand. In order to complete this project successfully, you will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, and optimization algorithms like gradient descent but want to understand how to use Keras Tuner to start optimizing hyperparameters for training their Keras models. You should also be familiar with the Keras API. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Requirements

Skills you will develop

  • Deep Learning

  • Machine Learning

  • Hyperparameter Optimization

  • hyperparameter tuning

  • keras

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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