Computer vision tasks such as image recognition have many useful applications, and seem somewhat magical when you think about them. But many smart folks have worked very hard indeed to make these fantastical techniques accessible. The goal of this post is to show you that this is a skill that you can learn, whatever your current level, and to present some pathways for you to pursue it if you’d like to learn how to solve these kinds of challenges for yourself.
No! Deep learning is very hard without a GPU. Fortunately, you can access a free GPU using Google Colab or one of the equivalents. You’ll see the starter notebook shared as a colab link: you should be able to run it from any computer with an internet connection, using Google’s computing power. Pretty neat :)
Different approaches work for different people. Some enjoy a bottom-up approach, learning the fundamentals and slowly working from the underlying mathematics up to a working model. For others, a top-down approach works better. This involves getting your hands on the tools, trying things out and then slowly filling in the gaps where needed. If you’re in the second category, you might prefer to start by trying out the starter notebook or browsing some other past submissions. There are also courses like course.fast.ai that cater to this way of learning. If you’re more formally inclined, we’ve included some resources to go deeper, and will try to keep adding to the list as more suggestions are shared by the community.
So grab the starter notebook and dive in, or browse some of the other resources shared here:
The starter notebook runs through an example submission, with extra explanation and a few exercises for you to practice with yourself.
Let us know which of your favourite resources we missed!