Large Scale Deep Learning Models

This course focuses on the methodologies and techniques used to train large models on extensive datasets across various data domains, including images, text, and audio. The course provides in-depth coverage of self-supervised learning approaches, which have become crucial for leveraging vast amounts of unlabeled data. Topics include data preprocessing and augmentation for different modalities, architectural considerations for scaling deep learning models, and strategies for distributed and parallel training.