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.
Comparing Validation Loss for Checkpointing
Model Checkpointing Using Training Loss
Fix Model Checkpointing in PyTorch
Completing Model Checkpointing in PyTorch
Model Checkpointing in PyTorch
Using Mini-Batches with the Wine Dataset in PyTorch
Change the Mini-Batch Size
Fix the Mini-Batch Training Bug
Implement Mini-Batch DataLoader
Train a PyTorch Model using Mini-Batches
Learning Rate Scheduler Configuration
Fine-Tuning the Learning Rate Scheduler
Fixing Learning Rate Scheduling
Updating Learning Rate in PyTorch
Learning Rate Scheduler Implementation
Adding Dropout to PyTorch Model
Adjust Weight Decay in Training
Fix Dropout Layer in PyTorch
Add Dropout and Regularization Layers
Mastering Dropout and Regularization in PyTorch