Convolutional Neural Networks (CNNs) are the most popular and widely used deep neural networks for computer vision problems. They are used in a variety of applications including image classification, face recognition, document analysis, medical image analysis, action recognition, and natural language processing. In this chapter, we will focus on learning convolutional operations, and concepts such as padding and strides, to optimize CNNs. The idea behind this chapter is to make you well versed with the functioning of the CNN and learn techniques such as data augmentation and batch normalization to fine-tune your network and prevent overfitting. We will also provide a brief discussion about how we can leverage transfer learning to boost model performance. 

In this chapter, we will cover the following recipes:

• Introduction to the convolution operation
• Understanding strides and padding
• Getting familiar with pooling layers
• Implementing transfer learning