Enhance Your Neural Style Transfer Skills Using PyTorch with this Coursera Program

Hewan Shrestha

08 June 2023

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Course Overview

Deep Learning with PyTorch: Neural Style Transfer, a 2-hour course, teaches you how to use PyTorch to implement Neural Style Transfer (NST). It is an optimization technique that takes a content image and a style picture and merges them so the output image looks exactly like the content image, but is painted exactly like the style picture.

You will learn to create an artistic style image from the content and the given style image followed by computing the style and content loss function. This loss function will be minimized using optimization techniques in order to create an artistic style image that preserves style and content features.

This project is designed for those who are interested in learning how to use PyTorch to implement NST. To be successful in this guided assignment, you must be familiar with the theory of NST, Python programming, and convolutional neural networks.

"Develop top skills required by the industry such as writing code, Python programming, Deep Learning applications, and more with the help of this PG program."

- Hewan Shrestha

Course Structure

It is a self-paced, well-curated intermediate-level course spread over 2 hours and taught online by experienced faculty members. The basics of Python were covered, followed by the implementation of neural networks using PyTorch for NST projects.

Parth Dhameliya, one of my instructors for this course, is a Machine Learning (ML) instructor at Coursera. His area of interest includes AI in healthcare, Deep Learning, ML and Data Science. He focuses more on building better medical diagnostic models and on creating computer vision-based projects.

The course covers a lot of interesting subjects, with (usually) good explanatory videos and walkthroughs. It will give you a lot of hands-on experience in writing code. The best part is that you will have working code that you can tweak and use for your own projects afterwards. Some of the top skills you will develop are: Image Processing, Deep Learning applications, CNN, Python Programming, and more.

Insider Tips

In order to get the best out of this course, below I have included an important tip that I think you might find useful:

Assessment and Grading Criteria

The assessment method requires completing assignments and undertaking projects under various modules throughout the degree. These can be research-based or concept specific. The evaluations are designed to ensure continuous student engagement with the program and to encourage learning. One could take the assessment a maximum of 3 times.

Final Take

As a Computer Science undergraduate, this course helped me get into the field of applied Deep Learning and in exploring the concerned niche to pave a lucrative career out of it. I’d recommend this course to all the beginners stepping into the domain as it would assist you in understanding the technicalities involved, both in terms of raw code constructs to the vivid demarcated arenas where it’s employed.