In this thesis we gave a detailed view of convolutional neural network models. In the first part of the thesis, we presented a gentle introduction to classic neural networks for better understanding, with some basic concepts of deep learning models. Second part of the thesis begins with the convolution operation description, which is the main carrier operation in convolutional neural networks. Furthermore, we described every CNN layer, and gave a mathematical background for the learning algorithm. After that, we presented some most common problems which can occur while training deep models. We described some optimization and regularization methods that can solve these problems and improve the model learning. Lastly, we described an implementation for neural style transfer problem, from an artistic image to a chosen photography. The model is implemented using TensorFlow machine learning library. Model is learned in Google Colaboratory environment which offers free GPU support. Results of the model computations are images, with style transferred from other images, as shown in the last chapter of the thesis.