In the age of deep learning, data scientists and machine learning engineers seldom create and train neural networks from scratch. A big chunk of what goes into performing a machine learning task, however, is collecting, preparing, and loading data to feed into a model.
In this session, using DataCamp Workspace we'll learn all about loading custom datasets into PyTorch and using transfer learning to perform an image processing task using a mostly-pretrained model, which we'll fine-tune.
We'll be using computer vision to answer the internet-popular question: is it a sloth or a pain au chocolat? This is a binary image classification task.
Maham KhanSenior Data Science Content Developer at DataCamp
Maham is a Senior Data Science Content Developer at DataCamp, on a mission to make data skills accessible for everyone.
Before joining DataCamp, she worked as a Data Scientist at the World Bank, exploring applications of data science for disaster risk reduction, poverty alleviation and climate change mitigation.
She has a background in Experimental Psychology and Philosophy from the University of Oxford, and Urban Data Science from New York University.