Convolutional Neural Networks (CNNs) are deep learning networks, which have achieved excellent results in many practical applications, and primarily in object recognition of images. CNN architecture is organized into a series of blocks. The first blocks are composed of two types of layers, convolutional layers and pooling layers; while the last blocks are fully-connected layers with softmax layers.

We'll develop two examples of CNN networks, for image classification problems. The first problem is the classic MNIST digit classification system. We'll see how to build a CNN that reaches 99 percent accuracy. The training set for the second example is taken from the Kaggle platform. The purpose here is to train a network on a series of facial images to classify their emotional stretch.

We'll evaluate the accuracy of the model and then we'll test it on a single image that does not belong to the original dataset.

The following topics are covered in the chapter:

• Introducing CNN networks
• CNN architecture
• A model for CNNs - LeNet
• Building your first CNN
• Emotion recognition with CNNs