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Deep Learning with PyTorch
Neural Style Transfer
Parth Dhameliya
Parth Dhameliya
In this 2 hour-long project-based course, you will learn to implement neural style transfer using PyTorch. Neural Style Transfer is an optimization technique used to take a content and a style image and blend them together so the output image looks like the...
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In this 2 hour-long project-based course, you will learn to implement neural style transfer using PyTorch. Neural Style Transfer is an optimization technique used to take a content and a style image and blend them together so the output image looks like the...
Read more
In this 2 hour-long project-based course, you will learn to implement neural style transfer using PyTorch. Neural Style Transfer is an optimization technique used to take a content and a style image and blend them together so the output image looks like the content image but painted in the style of the style image. We will create artistic style image using content and given style image. We will compute the content and style loss function. We will minimize this loss function using optimization techniques to get an artistic style image that retains content features and style features.
This guided project is for learners who want to apply neural style transfer practically using PyTorch.
In order to be successful in this guided project, you should be familiar with the theoretical concept of neural style transfer, python programming, and convolutional neural networks.A google account is needed to use the Google colab environment.
Register for this course and see more details by visiting:
OpenCourser.com/course/urxde9/deep
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches learners to use PyTorch for practical applications
Focuses on transferring style using neural networks, a key concept in AI
Hands-on with a strong emphasis on computing style and content loss functions
Suitable for students with introductory knowledge of neural style transfer and computer vision
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Save Deep Learning with PyTorch : Neural Style Transfer to your list so you can find it easily later:
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Reviews summary
Neural style transfer with pytorch
This project-based course teaches learners how to implement neural style transfer using PyTorch. Students who are already familiar with the theoretical concept of neural style transfer, python programming, and convolutional neural networks are likely to succeed in this course. Reviews highlight that the course content is well-structured, but some mention that there is little explanation about why some layers were chosen and that the codelab is slow.
Practical and understandable lessons
"The lessons are practical and understandable"
Well-structured content
"Excellent course with great structure"
Codelab is super slow
"codelab is super slow"
Lacked a bit of depth
"It lacked a bit of depth"
Activities
Coming soon
We're preparing activities for
Deep Learning with PyTorch : Neural Style Transfer.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Deep Learning with PyTorch : Neural Style Transfer will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and maintain deep learning models. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision algorithms and systems. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models, and deploy them to production. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Bioinformatics Scientist
Bioinformatics Scientist
Bioinformatics Scientists develop and apply computational tools to analyze biological data. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course will help you to understand the theoretical concept of neural style transfer, as well as how to implement it using PyTorch. This will give you the skills you need to develop and deploy machine learning models for a variety of applications, including image processing.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and organizations. This course may be useful for understanding the theoretical concept of neural style transfer, which may be helpful in developing financial models.
Project Manager
Project Manager
Project Managers develop and implement project plans. This course may be useful for understanding the theoretical concept of neural style transfer, which may be helpful in developing project timelines and budgets.
Sales Manager
Sales Manager
Sales Managers develop and implement sales strategies. This course may be useful for understanding the theoretical concept of neural style transfer, which may be helpful in developing sales presentations.
Business Analyst
Business Analyst
Business Analysts develop and implement business strategies. This course may be useful for understanding the theoretical concept of neural style transfer, which may be helpful in developing business cases and recommendations.
Marketing Manager
Marketing Manager
Marketing Managers develop and implement marketing strategies. This course may be useful for understanding the theoretical concept of neural style transfer, which may be helpful in developing marketing campaigns.
Product Manager
Product Manager
Product Managers develop and implement product strategies. This course may be useful for understanding the theoretical concept of neural style transfer, which may be helpful in developing product requirements.
For more career information including salaries, visit:
OpenCourser.com/course/urxde9/deep
Reading list
We've selected 12 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
Deep Learning with PyTorch : Neural Style Transfer.
Provides a comprehensive overview of pattern recognition and machine learning. It covers the different types of pattern recognition and machine learning algorithms, their applications, and their theoretical foundations.
Save
Provides a comprehensive overview of machine learning. It covers the different types of machine learning algorithms, their applications, and their theoretical foundations.
Provides a comprehensive introduction to PyTorch, the deep learning library used in the course. It covers the basics of PyTorch and provides practical examples for building and training neural networks.
Save
Provides a comprehensive overview of deep learning. It covers the different types of deep learning models, their architectures, and their applications.
Provides a practical introduction to machine learning using Python. It covers the different types of machine learning algorithms, their applications, and how to implement them in code.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning algorithms and techniques.
Save
Provides a comprehensive overview of neural networks. It covers the different types of neural networks, their architectures, and their applications.
Provides a practical introduction to machine learning for programmers. It covers the different types of machine learning algorithms, their applications, and how to implement them in code.
Provides a comprehensive overview of deep learning for computer vision. It covers a wide range of topics, including image classification, object detection, and image segmentation.
Save
Provides a comprehensive overview of Generative Adversarial Networks (GANs). It covers the theory behind GANs, different types of GANs, and their applications.
Provides a theoretical foundation for machine learning. It covers probability theory, Bayesian inference, and graphical models.
Provides a rigorous introduction to machine learning theory. It covers the fundamental concepts of machine learning and provides mathematical proofs for many of the algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/urxde9/deep
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Good to know
Teaches learners to use PyTorch for practical applications
Focuses on transferring style using neural networks, a key concept in AI
Hands-on with a strong emphasis on computing style and content loss functions
Suitable for students with introductory knowledge of neural style transfer and computer vision
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