Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
Login to see the comments