PyTorch Techniques for Model Optimization | CodeSignal Learn

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PyTorch Techniques for Model Optimization

Explore advanced PyTorch techniques to boost model performance. Learn about regularization, dropout to avoid overfitting, batch normalization for stable and quick training, and efficient training through learning rate scheduling. Also, discover how to save the best model with checkpointing. Each concise module offers practical skills to improve your machine learning projects.

Python

PyTorch

4 lessons

20 practices

Less than 3 hours

Model Validation and Selection

Course details

Saving Progress with Model Checkpointing in PyTorch

Comparing Validation Loss for Checkpointing

Model Checkpointing Using Training Loss

Fix Model Checkpointing in PyTorch

Completing Model Checkpointing in PyTorch

Model Checkpointing in PyTorch

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