Introduction to Deep Learning for Image Classification | Cindy Gonzales
About This Video
Image classification is a task in the Computer Vision domain that takes in an image as input and outputs a label for that image. Deep learning is the most effective modern method for modeling this task. In this interactive workshop, we will walkthrough a Jupyter Notebook which will overview how to perform multi-class image classification in Python using the PyTorch library. The intention is to give the audience a broad overview of this task of classification and inspire participants to explore the vast fields of visual recognition and computer vision at large.
This workshop was conducted by Cindy Gonzales, Data Science Team Lead for the Biosecurity and Data Science Applications Group at Lawrence Livermore National Laboratory
Useful resources for this workshop:
In This Video
Data Science Team Lead for the Biosecurity and Data Science Applications Group, Lawrence Livermore National Laboratory
Cindy Gonzales is the Data Science Team Lead for the Biosecurity and Data Science Applications (BiDS) group at Lawrence Livermore National Laboratory (LLNL). She originally joined LLNL as an administrator, onboarding and offboarding summer students during their internships. After attending a machine learning seminar, she was inspired to embark on a data science career and now works as a data scientist in the Global Security Computing Applications Division. Her research interests include using machine learning to detect objects in unconventional types of imagery, such as overhead, multi-modal, and radar imagery. She earned her B.S. in Statistics from California State University, East Bay and currently pursuing her M.S. in Data Science from Johns Hopkins University which she plans to complete in August 2022. Cindy is also involved in several initiatives that promote diversity and inclusion including serving as a Co-Chair of the Lawrence Livermore Lab’s Women’s Association New Moms Group and co-Ambassador for WiDS Livermore.