Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, one might want to include an image classifier in a smart phone app. To do this, one would use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
In this project, we'll train an image classifier to recognize different species of flowers. We can imagine using something like this in a phone app that tells us the name of the flower our phone's camera is looking at. In practice we'd train this classifier, then export it for use in our application. We'll be using this dataset of 102 flower categories, we can see a few examples below.
The project is broken down into multiple steps:
- Load and preprocess the image dataset
- Train the image classifier on our dataset
- Use the trained classifier to predict image content