figure - italian street image style transfer at the firsttenth and twentieth iteration the results depicted in figure - are definitely pleasure to look at and give the feeling of an entire city underwaterwe encourage you to use images of your own with this same framework also feel free to experiment with leveraging different convolution layers for the style and content feature representations as mentioned in gatys et al and johnson et al ##note the concept and details of neural style transfer were introduced and explained by gatys et al and johnson et al in their respective papers available at chollet as well as harish narayanan' excellent blog for detailed step-by-step guide on neural style transfers ummary this presented topics from the very forefront of the machine learning landscape through this we utilized our learnings about machine learning in general and deep learning in particular to understand the concepts of image classificationtransfer learningand style transfer the started off with quick brush up of concepts related to convolutional neural networks and how they are optimized architectures to handle image related data we then worked towards developing image classifiers the first classifier was developed from scratch and with the help of keras we were able to achieve decent results the second classifier utilized pre-trained vgg- deep cnn model as an image feature extractor the pre-trained model based classifier helped us understand the concept of transfer learning and how it is beneficial the closing section of the introduced the advanced topic of neural style transferthe main highlight of this style transfer is the process of applying the style of reference image to specific target image such that in the processthe original content of the target image remains unchanged this process utilizes the potential of cnns to understand image features at different granularities along with transfer learning based on our understanding of these concepts and the research work by gatys et al and johnson et al we provided step-by-step guide to implement system of neural style transfer we concluded the section by presenting some amazing results from the process of neural style transfer deep learning is opening new doors every day its application to different domains and problems is showcasing its potential to solve problems previously unknown machine learning is an ever evolving and very involved field through this bookwe traveled from the basics of machine learning frameworkspython ecosystem to different algorithms and concepts we then covered multiple use cases across showcasing different scenarios and ways problem can be solved using the tools from the machine learning toolbox the universe of machine learning is expanding at breakneck speedsour attempt here was to get you started on the right trackon this wonderful journey