Deep Learning

Harvard FAS

This graduate course introduces the principles and practice of deep learning, from neural network training to foundation models and agents. Topics include automatic differentiation, optimization, convolutional and sequence models, transformers, generative modeling, data and scaling, distributed training, adaptation and post-training, efficient inference, and reasoning with tools and memory. Applications in language, vision, robotics, and biology illustrate how modeling choices, learning…