This chapter introduces the first deep learning architecture of the book, convolutional neural networks. It starts with redefining the way a logistic regression accepts data, and defines 1D and 2D convolutional layers as a natural extension of the logistic regression. The chapter also details on how to connect the layers and dimensionality problems. The local receptive field is introduced as a core concept of any convolutional architecture and the connections with the vanishing gradient problem is explored. Also the idea of padding is introduced in the visual setting, as well as the stride of the local receptive field. Pooling is also explored in the general setting and as max-pooling. A complete convolutional neural network for classifying MNIST is then presented in Keras code, and all the details of the code are presented as comments and illustrations. The final section of the chapter presents modifications needed to adapt convolutional networks, which are primarily visual classificators, to work with text and language.