Where Now?

We are about to reach the end of this course, but you are up for more. There is no shortage of things to learn. In fact, even if we track the field closely, keeping up with the barrage of exciting new ideas and techniques across the many areas of deep learning is hard.

With so many possible paths to mastery, we might wonder which one to take. The next sections describe a few of those paths, including some topics we have not covered so far.

The path of vision

Our first option is to follow the path of vision that we discussed in the previous lessons: computer vision and CNNs. We picked image recognition as the storyline for this course because it makes for nice concrete examples, but even then, we only scratched the surface.

There is a lot more to do and learn in computer vision beyond recognizing images. One prominent subfield of computer vision these days is object detection. While image recognition answers questions like: “Does this picture represent a platypus?” object detection answers questions like: “Where are the platypuses in this picture?” As you can imagine, that’s a crucial technology for self-driving cars.

Computer vision is not just about static images but also about video. There are many fascinating use cases for computer vision applied to video, including pose estimation, which detects the position of a human figure, and motion estimation, which tracks the movement of objects.

To delve deeper into computer vision, we should learn more about CNNs. We may look up a technique called transfer learning, which allows us to reuse a pre-trained model on a different dataset. Transfer learning allows us to download a model that might have been trained on a large cluster of GPUs and complete the training on our home machine. That technique can be useful in all areas of supervised learning, but it’s most commonly used with CNNs.

The path of language

Another area of ML that’s in full bloom these days is natural language processing, such as speech or text. Just like ...