Chevron Left
Retour à Hyperparameter Tuning with Keras Tuner

Avis et commentaires pour d'étudiants pour Hyperparameter Tuning with Keras Tuner par Coursera Project Network

63 évaluations

À propos du cours

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....

Meilleurs avis

Filtrer par :

1 - 7 sur 7 Avis pour Hyperparameter Tuning with Keras Tuner

par Onyero W O

2 janv. 2022

Very beneficial for deep learning with Keras practitioners. I loved it, and will be using it as a reference subsequently.

par pranay s

29 sept. 2021

loved it hope to find new courses like this

par Sahil V

20 juin 2021

Helpful foundation course for Keras Tuner.

par Saharsh S

28 mars 2022

The manner it was explanined was amazing

par Lam C V D

4 janv. 2021

Too complicated

par Mario E S M

1 juin 2022


par Rohit B

3 août 2022

The course is well taught. A bit more insight in to concepts would have been better. And it need to be updated to the laters version of Keras tuner. The kerastuner used in this project is now deprecated.