In this chapter, the recent advances in image processing with deep learning models were introduced. We started by discussing the basic concepts of deep learning, how it's different from traditional ML, and why we need it. Then CNNs were introduced as deep neural networks designed particularly to solve complex image processing and computer vision tasks. The CNN architecture with convolutional, pooling, and FC layers were discussed. Next, we introduced TensorFlow and Keras, two popular deep learning libraries in Python. We showed how test accuracy on the MNIST dataset for handwritten digits classification can be increased with CNNs, then the same using FC layers only. Finally, we discussed a few popular networks such as VGG-16/19, GoogleNet, and ResNet. Kera's VGG-16 model was trained on Kaggle's Dogs vs. Cats competition images and we showed how it performs on the validation image dataset with decent accuracy.

In the next chapter, we'll discuss how to solve more complex image processing tasks (for example, object detection, segmentation, and style transfer) with deep learning models and how to use transfer learning to save training time.