Summary

In this chapter, we studied why we need computer vision and how it works. We understood why computer vision is one of the hottest fields in machine learning. Then, we worked with convolutional neural networks, their architecture, and how we can build CNNs in real-life applications. We also tried to improve our algorithms by adding more ANN and CNN layers and by changing activation and optimizer functions. We also tried different activation functions and loss functions. In the end, we were able to successfully classify new images of cats and dogs through the algorithm. Remember, the images of dogs and cats can be substituted with any other images, such as tigers and deer, or MRI scans of brains with and without a tumor. Any binary-classification computer-imaging problem can be solved with the same approach.

In the next chapter, we will study an even more efficient technique for working on computer vision, which is less time-consuming and easier to implement.