- Level Professional
- المدة 3 ساعات hours
- الطبع بواسطة Coursera Project Network
-
Offered by
عن
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.الوحدات
Hyperparameter Tuning with Keras Tuner
1
Assignment
- Graded Quiz: Test your Project Understanding
1
Labs
- Hyperparameter Tuning with Keras Tuner
1
Readings
- Project-based Course Overview
Auto Summary
Explore the "Hyperparameter Tuning with Keras Tuner" course, led by Coursera, designed for IT & Computer Science enthusiasts. In this 2-hour guided project, master the use of Keras Tuner to optimize hyperparameters for Keras models. Ideal for those with Python programming experience and a solid understanding of Neural Networks and optimization algorithms. Perfect for learners in North America, with free subscription options available. Enhance your practical skills in hyperparameter tuning without delving into algorithm implementation. Join now to elevate your machine learning expertise!

Instructor
Amit Yadav