Week #7 - Efficient training of neural networks

In today's lecture, we'll explore essential techniques for efficiently training and evaluating neural networks, with a particular focus on understanding and interpreting learning curves. We'll examine how to diagnose common training issues, optimize the learning process, and develop intuition for model behavior through careful analysis of loss and accuracy metrics throughout the training cycle.

Learning objectives:

• Master the interpretation of loss and accuracy curves to diagnose model learning behavior and common problems
• Understand the relationship between training and validation metrics, and their implications for model generalization
• Identify and address common training issues including underfitting, overfitting, and learning rate problems through curve analysis
• Learn practical techniques for monitoring and visualizing neural network training progress
• Explore methods for early stopping and learning rate scheduling based on performance metrics
• Apply diagnostic tools and visualization techniques to real-world model development scenarios
• Implement efficient training practices including batch normalization and gradient clipping

Laboratory - TBA

Resources - TBA