In this section, we will look at how computer vision has grown over the past couple of years into the current field of computer vision we have today. As mentioned before, the progress in the field of deep learning is what propelled computer vision to advance.

Deep learning has enabled a lot of applications that seemed impossible before. These include the following:

• Autonomous driving: An algorithm is able to detect the location of pedestrians and other cars, helping to make decisions about the direction of the vehicle and avoid accidents.
• Face recognition and smarter mobile applications: You may already have seen phones that can be unlocked using facial recognition. In the near future, we could have security systems based on this; for example, the door of your house may be unlocked by your face or your car may start after recognizing your face. Smart mobile applications with fancy features such as applying filters and grouping faces together have also improved drastically.
• Art generation: Even generating art will be possible, as we will see during this book, using computer vision techniques.

What is really exciting is that we can use some of these ideas and architectures to build applications.

The main source of knowledge for deep learning algorithms is data. Therefore, the quality and the amount of data greatly affects the performance of every algorithm.

For speech recognition, we have a decent amount of data, considering the complexity of the problem. Although the dataset for the images has dramatically improved, having a few more samples will help achieve better results for image recognition. On the other hand, when it comes to object detection, we have less data due to the complexity in the effort of marking each of the objects with a bounding box as shown in the diagram.

Computer vision is, in itself, a really complex problem to solve. Imagine having a bunch of pixels with decimal values, and from there, you have to figure out what they represent.

For this reason, computer vision has developed more complex techniques, larger and more complex architectures, and also a lot of parameters to tune. The rule is such that the less data you have, the more hacks are needed, the more engineering or manual creation of features is required, and the architectures tend to grow complex. On the other hand, if you have more data, the deep learning algorithm tends to do well, and hand-engineering the data becomes a whole lot easier, which means we don't have to tune the parameters and the network architectures stay simple.

Throughout this book, we'll look at several methods to tackle computer vision challenges, such as transfer learning using well-known architectures in literature and opera. We will also make good use of open source implementations. In the next section, we'll start to understand the basics of neural networks and their representations.