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Amit Yadav

In this 2-hour long project-based course, you will learn the basics of Neural Style Transfer with TensorFlow. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content's overall structure and complex features. We will see how to create content and style models, compute content and style costs and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features from another image.

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In this 2-hour long project-based course, you will learn the basics of Neural Style Transfer with TensorFlow. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content's overall structure and complex features. We will see how to create content and style models, compute content and style costs and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features from another image.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

Neural Style Transfer
Welcome to this project-based course on Neural Style Transfer with TensorFlow. In this project, you will apply a style image's stylistic features to a content image while retaining the overall structure of the content image, and you will do this with the help of a neural network. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content’s overall structure and complex features. We will see how to create content and style models, compute content and style costs, and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Beginners will learn about the basics of Neural Style Transfer with TensorFlow
Learners will apply the stylistic features of a style image to a content image while retaining the content's overall structure
This project is an excellent resource for gaining practical experience with Neural Style Transfer
Learners will utilize Jupyter, Tensorflow, and Python pre-installed on cloud desktops for the project
Course access requires a strong internet connection

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Reviews summary

Well-received tensorflow course

Learners say this TensorFlow course about neural style transfer is well-received. Hands-on assignments are engaging and help students apply their knowledge of neural networks. The instructor provides clear and detailed explanations, making the course easy to understand. Students appreciate the course's practical approach, which provides them with the skills they need to implement neural style transfer models.
Instructor provides clear explanations.
"Excellent and precise explanation."
"Nice hands on course"
"nice explanation by amit sir"
Hands-on assignments help students learn.
"I will give 4.6/5 the project.This project gives me the opportunity to learn my long desired NST work."
"Good Analysis"
"Great Learning"
Course focuses on practical skills.
"The project provides the absolute necessary explanations to implement a neural style transfer model, without going too deep into the mathematical concepts or the implementation of the auxiliary libraries used."
"This was a great project. Explanations were given nicely."
"simply awesome"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Neural Style Transfer with TensorFlow with these activities:
Review linear algebra and calculus
Review the fundamentals of linear algebra and calculus to reinforce your mathematical foundation for understanding neural networks.
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  • Revisit textbooks or online resources to cover the basics of linear algebra (e.g., vectors, matrices, transformations).
  • Practice solving calculus problems (e.g., derivatives, integrals) to enhance your analytical skills.
Follow tutorials on TensorFlow and neural networks
Get hands-on experience with TensorFlow and neural networks through guided tutorials, strengthening your understanding of the core concepts.
Browse courses on TensorFlow
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  • Find online tutorials or courses on TensorFlow and neural networks.
  • Follow the tutorials step-by-step, building and training your own neural networks.
  • Experiment with different architectures and parameters to observe their impact on performance.
Organize a study group or discussion forum
Deepen your understanding by engaging in discussions and sharing knowledge with peers, fostering a collaborative learning environment.
Browse courses on Neural Style Transfer
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  • Identify a topic or area of neural style transfer for discussion.
  • Invite classmates or fellow learners to join your study group or forum.
  • Facilitate discussions, encourage active participation, and share resources.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with experts in neural style transfer
Enhance your learning by seeking guidance from experienced practitioners in neural style transfer, gaining valuable insights and expanding your professional network.
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  • Attend conferences or workshops related to neural style transfer.
  • Reach out to researchers or industry professionals in the field.
  • Engage with online communities or forums dedicated to neural style transfer.
Attend workshops or conferences on neural style transfer
Expand your knowledge and connect with experts in the field through workshops or conferences dedicated to neural style transfer, gaining valuable insights and staying abreast of the latest advancements.
Browse courses on Neural Style Transfer
Show steps
  • Research and identify relevant workshops or conferences.
  • Register and attend the event.
  • Actively participate in sessions, ask questions, and network with attendees.
Practice implementing neural style transfer algorithms
Solidify your understanding of neural style transfer by implementing the algorithms from scratch in TensorFlow, enabling you to customize and experiment with the technique.
Browse courses on Neural Style Transfer
Show steps
  • Start with a simple example of neural style transfer.
  • Experiment with different loss functions and optimization algorithms.
  • Implement advanced techniques such as perceptual loss or feature matching.
Write a blog post or article on neural style transfer
Solidify your understanding by explaining neural style transfer to others, fostering your ability to articulate complex technical concepts clearly.
Browse courses on Neural Style Transfer
Show steps
  • Choose an aspect of neural style transfer to focus on.
  • Research and gather information from credible sources.
  • Write a well-structured and engaging blog post or article.
Develop a neural style transfer application
Synthesize your knowledge by creating a functional neural style transfer application, demonstrating your ability to apply the technique in a practical setting.
Browse courses on Neural Style Transfer
Show steps
  • Design the user interface and functionality of your application.
  • Integrate the neural style transfer algorithm into your application's codebase.
  • Test and refine your application to ensure it meets user requirements.

Career center

Learners who complete Neural Style Transfer with TensorFlow will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
The field of computer vision is rapidly growing, with new applications being developed in fields such as image recognition, object tracking, and robotics. Neural Style Transfer is a technique that can be used to improve the performance of computer vision systems by allowing them to learn the style of a particular image or video. This technique can be used to create more accurate and efficient computer vision systems. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your computer vision applications.
Machine Learning Engineer
Neural Style Transfer is a technique that can be used to improve the performance of machine learning models by allowing them to learn the style of a particular dataset. This technique can be used to create more accurate and efficient machine learning models. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your machine learning applications.
Data Scientist
Neural Style Transfer is a technique that can be used to analyze and extract features from images. This technique can be used to create more accurate and efficient data science models. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your data science applications.
Software Engineer
Neural Style Transfer is a technique that can be used to create new and innovative software applications. This technique can be used to create software applications that are more visually appealing and engaging. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your software applications.
Computational Photographer
Neural Style Transfer is a technique that can be used to create new and innovative computational photography. This technique can be used to create computational photography that is more visually appealing and engaging. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your computational photography.
Robotics Engineer
Neural Style Transfer is a technique that can be used to improve the performance of robotics systems by allowing them to learn the style of a particular environment. This technique can be used to create more accurate and efficient robotics systems. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your robotics applications.
Computer Graphics Artist
Neural Style Transfer is a technique that can be used to create new and innovative computer graphics. This technique can be used to create computer graphics that are more realistic and visually appealing. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your computer graphics.
Artificial Intelligence Researcher
Neural Style Transfer is a technique that can be used to improve the performance of artificial intelligence systems by allowing them to learn the style of a particular dataset. This technique can be used to create more accurate and efficient artificial intelligence systems. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your artificial intelligence applications.
Web Developer
Neural Style Transfer is a technique that can be used to create new and innovative web applications. This technique can be used to create web applications that are more visually appealing and engaging. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your web applications.
Graphic designer
Neural Style Transfer is a technique that can be used to create new and innovative graphic designs. This technique can be used to create graphic designs that are more visually appealing and engaging.
Data Analyst
Neural Style Transfer is a technique that can be used to analyze and extract features from data. This technique can be used to create more accurate and efficient data analysis models. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your data analysis applications.
Game Developer
Neural Style Transfer is a technique that can be used to create new and innovative games. This technique can be used to create games that are more visually appealing and engaging. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your games.
Marketing Manager
Neural Style Transfer is a technique that can be used to create new and innovative marketing campaigns. This technique can be used to create marketing campaigns that are more visually appealing and engaging.
Photographer
Neural Style Transfer is a technique that can be used to create new and innovative photographs. This technique can be used to create photographs that are more visually appealing and engaging. This course will provide you with the skills you need to develop and use Neural Style Transfer techniques in your photography.
Digital Artist
Neural Style Transfer is a technique that can be used to create new and innovative digital art. This technique can be used to create digital art that is more visually appealing and engaging.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Neural Style Transfer with TensorFlow.
Provides a comprehensive overview of deep learning concepts and techniques. It covers the basics of neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. It also includes practical examples and exercises to help readers apply deep learning to their own projects.
Provides a comprehensive overview of generative adversarial networks (GANs). It covers the basic concepts of GANs, different types of GANs, and applications of GANs. It also includes practical examples and exercises to help readers implement GANs.
Provides a comprehensive overview of computer vision algorithms and techniques. It covers topics such as image formation, feature detection, object recognition, and video analysis. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning algorithms. It covers topics such as linear regression, logistic regression, and support vector machines. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of deep learning for computer vision. It covers topics such as image classification, object detection, and image segmentation. It valuable resource for anyone who wants to learn more about deep learning for computer vision.
Provides a comprehensive overview of deep learning for natural language processing. It covers topics such as text classification, machine translation, and question answering. It valuable resource for anyone who wants to learn more about deep learning for natural language processing.
Provides a comprehensive overview of machine learning concepts and techniques. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of deep reinforcement learning. It covers topics such as Markov decision processes, value functions, and policy gradients. It valuable resource for anyone who wants to learn more about deep reinforcement learning.

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