Summary

In this chapter, we studied why we need computer vision and how it works. We learned why computer vision is one of the hottest fields in machine learning. Then, we worked with convolutional neural networks, learned about their architecture, and looked at 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 the activation and optimizer functions. Finally, we tried out different activation functions and loss functions.

In the end, we were able to successfully classify new images of cars and flowers through the algorithm. Remember, the images of cars and flowers 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...