Artistic style transfer is an algorithm proposed by Gatys et al. In A Neural Algorithm of Artistic Style, the authors talk about the difficulties in segregating the content and style of an image. The content of an image refers to the discernible objects in an image. The style of an image, on the other hand, refers to the abstract configurations of the elements in the image that make it unique. The style and content segregation is difficult because of the unavailability of representations that hold the semantic understanding of images. Now, due to the advancement of convolutional neural networks, such semantic representations are possible. This is part one of the two. This report will be structured as follows: 1. Understanding deep image representations by inverting them. 2. Normalized VGG16. 3. Content representations. 4. Amalgamation.