Efficient AI Model Training with PyTorch

Training programme overview

Master efficient model training in PyTorch: data handling, optimized training loops, hyperparameter tuning, parallelism and best practices to speed convergence and lower compute cost.

The course covers the PyTorch training pipeline: data pipelines, dataloaders, augmentation strategies and efficient batching.

It addresses optimizers, learning rate schedules, checkpointing and acceleration techniques (FP16, gradient accumulation, distributed training).

Methods for monitoring training, diagnosing overfitting and ensuring experiment reproducibility are also presented.

Training · Intermediate