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.
Overview
Syllabus
- Lesson 1: Saving Progress with Model Checkpointing in PyTorch
- Lesson 2: Model Training with Mini-Batches in PyTorch
- Lesson 3: Learning Rate Scheduling in PyTorch
- Lesson 4: Overfitting Prevention with Regularization and Dropout
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