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
- Unit 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
- Unit 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
- Unit 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
- Unit 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
Related Courses
Related Articles
-
[2026] Generative AI Mastery: 900+ Courses to Develop Your AI Superpowers
-
[2026] Unlock 2000+ Free Certificates: Master Tech & Soft Skills with CodeSignal Learn
-
Become a Supercommunicator: Practical Skills for Better Conversations
-
CodeSignal – The Duolingo of Coding Practice
-
12 Best Deep Learning Courses for 2026
-
From Zero to GenAI: 9 Unique Ways to Understand Large Language Models
