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
1. Recognize and mitigate overfitting in neural networks
- Identify signals of overfitting in training metrics and validation performance
- Implement early stopping to prevent overfitting
- Apply learning rate schedules to improve training stability
- Monitor validation metrics to assess generalization performance
2. Apply regularization techniques
- Implement L1 and L2 regularization to constrain model complexity
- Apply dropout as a form of stochastic regularization
- Use batch normalization to stabilize and accelerate training
- Compare the effects of different regularization approaches
3. Perform hyperparameter tuning
- Identify key hyperparameters that impact model performance
- Implement grid search and random search for hyperparameter optimization
- Use cross-validation to evaluate hyperparameter configurations
- Apply Keras Tuner for efficient hyperparameter optimization
Guided Project
Tuning and Regularizing Neural Networks
Guided Project File:
DS_423_Tune_Lecture.ipynb
Module Assignment
Please read the assignment file in the GitHub repository for detailed instructions on completing your assignment tasks.
Assignment File:
DS_423_Tune_Assignment.ipynb
In this assignment, you will continue working with neural networks for sketch classification. Your tasks include:
- Implementing and comparing different regularization techniques (L1, L2, dropout)
- Using early stopping callbacks to prevent overfitting
- Implementing learning rate schedules
- Applying batch normalization to stabilize training
- Using Keras Tuner to optimize hyperparameters
- Analyzing the impact of various regularization strategies on model performance
Assignment Solution Video
Check for Understanding
Complete the following items to test your understanding:
- Compare and contrast L1 and L2 regularization techniques
- Explain how dropout works as a regularization method and when to use it
- Describe how batch normalization affects neural network training
- Identify the trade-offs between grid search and random search for hyperparameter tuning
- Explain how learning rate schedules can improve model convergence
- Describe the concept of early stopping and its implementation with Keras callbacks