Machine Learning
770 learners
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
3 hours
Badge for Model Validation and Selection,
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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