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.

Lessons and practices

Lesson 1: 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

Lesson 2: Model Training with Mini-Batches 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

Lesson 3: Learning Rate Scheduling in PyTorch

Learning Rate Scheduler Configuration

Fine-Tuning the Learning Rate Scheduler

Fixing Learning Rate Scheduling

Updating Learning Rate in PyTorch

Learning Rate Scheduler Implementation

Lesson 4: Overfitting Prevention with Regularization and Dropout

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

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