In this workshop, you will learn the basics of training a neural network in PyTorch. First, we will go over neurons, forward- and backward-pass, loss, and optimizers. Second, we will discuss loading up custom data into PyTorch using the Dataset/DataLoader classes. Third, we will build a dense neural network and train the model using a Dataset/DataLoader. Finally, we will discuss and then fine-tune a convolutional neural network model.
NOTE: Seats are limited. Preference will be given to faculty, students, and researchers in the schools of the health sciences (Medicine, Dental Medicine, Pharmacy, Nursing, Health and Rehabilitation Sciences, and Public Health).