Grid searching is generally not an operation that we can perform with deep learning methods. This is because deep learning methods often require large amounts of data and large models, together resulting in models that take hours, days, or weeks to train. By Jason Brownlee.
This tutorial will guide you through:
- How to develop a generic grid searching framework for tuning model hyperparameters
- How to grid search hyperparameters for a Multilayer Perceptron model on the airline passengers univariate time series forecasting problem
- How to adapt the framework to grid search hyperparameters for convolutional and long short-term memory neural networks
- Train-test split
- Series as supervised learning
- Walk-Forward validation
- Repeat evaluation
- Summarize performance
- Worked example
Among other things you will develop a grid search test harness that can be used to evaluate a range of hyperparameters for different neural network models, such as MLPs, CNNs, and LSTMs. There is plenty of links attached for you to learn even more. Excellent!
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