In recent years, artificial intelligence has rapidly advanced and has become one of the most important topics in the modern world. One branch of artificial intelligence has stood out as really influential – deep learning. Deep learning is a powerful tool within artificial intelligence that has allowed for the creation of artificial neural networks, and with them, convolutional neural networks. Convolutional neural networks are highly effective in analyzing and recognizing visual patterns in images, which can be seen in neural style transfer. Neural style transfer is a technique that combines the style of one image with the content of another, resulting in a stylized and visually appealing image. The aim of this thesis was to provide a theoretical framework and a comprehensive understanding of the concept of neural style transfer. This thesis is comprised of two parts: a theoretical and a practical part. The theoretical part provides an overview of artificial intelligence and machine learning, as well as an overview of the concept of deep learning. Also, it delves into the theoretical and practical aspects of neural style transfer. The practical part of the thesis is concerned with the implementation of neural style transfer using a pre-trained model. This implementation is done using the TensorFlow machine learning library in the Google Colaboratory environment. The results of the implementation are presented in the last part of the thesis.