Deep learning is inducing revolutionary changes across many disciplines. It is also becoming more accessible to domain experts and AI enthusiasts with the advent of libraries like TensorFlow, PyTorch, and now fastai. With the mission of democratizing deep learning, fastai is a research institute dedicated to helping everyone from a beginner level coder to a proficient deep learning practitioner to achieve world-class results with state-of-the-art models and techniques from the latest research in the field. This blog post will walk you through the process of developing a dog classifier using fastai. The goal is to learn how easy it is to get started with deep learning models and be able to achieve near-perfect results with a limited amount of data using pre-trained models.

If you are already using a pre-curated dataset, such as Labeled Faces in the Wild (LFW), then the hard work is done for you. You'll be able to use next week's blog post to create your facial recognition application. But for most of us, we'll instead want to recognize faces that are not part of any current dataset and recognize faces of ourselves, friends, family members, coworkers and colleagues, etc. To accomplish this, we need to gather examples of faces we want to recognize and then quantify them in some manner. This process is typically referred to as facial recognition enrollment. We call it "enrollment" because we are "enrolling" and "registering" the user as an example person in our dataset and application.

An example of a Pokedex (thank you to Game Trader USA for the Pokedex template!) When I was a kid, I was a huge Pokemon nerd. I collected the trading cards, played the Game Boy games, and watched the TV show. If it involved Pokemon, I was probably interested in it. Pokemon made a lasting impression on me -- and looking back, Pokemon may have even inspired me to study computer vision.