Module 3: Tune
Module Overview
This module focuses on hyperparameter tuning and regularization techniques for neural networks. You'll learn how to optimize model performance by finding the best hyperparameter configurations and implementing strategies to prevent overfitting. The module covers various regularization approaches, hyperparameter search methods, and techniques for monitoring and improving model generalization. By mastering these concepts, you'll be able to develop neural networks that perform well on both training and validation data.
Learning Objectives
- Describe the major hyperparameters to tune
- Implement an experiment tracking framework
- Search the hyperparameter space with keras-tuner
Guided Project
Open DS_423_Tune_Lecture.ipynb in the GitHub repository to follow along with the guided project.
Module Assignment
Continue using TensorFlow Keras and the Quickdraw dataset to build a sketch classification model, now focusing on hyperparameter tuning. Use GridSearchCV and keras-tuner libraries to systematically search for optimal hyperparameters and improve model performance.
Assignment Solution Video
Additional Resources
Hyperparameter Tuning
- Keras Tuner Documentation
- TensorFlow: Hyperparameter Tuning with Keras Tuner
- Scikit-learn: Grid Search Documentation
Regularization and Overfitting
- TensorFlow: Keras Regularizers Documentation
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- Early Stopping for Neural Networks