Alan Fortuny Sicart

Introduction and key lessons learned  In the second lesson of the course practical deep learning for coders with fast ai, we get our hands dirty with our first image classifier on the data and topic we like. There are already many good learning and insights from this lesson I would like to share before I jumped into explaining the workflow: The first finding is that the following workflow works for a wide range of problems, I did get very good results classifying tree species, mushrooms types and running shoes. Second, we do not need to label the data manually, we can leverage from example the Bing API for that purpose,alhtough we need to be very accurate and specific with the search term Third, we can get very good results even with around 150 images per class, thanks to the fact that we are doing transfer learning and that we perform data augmentations on the data Four, one can increase their training size by a factor of 10  performing data augmentations that help to generalize the m