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Snehan Kekre
Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.
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...
Read more
Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.
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...
Read more
Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.
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 Keras pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/i9tgr7/image
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops strong foundations for intermediate learners, who can then move on to more advanced projects
Teaches learners in-demand tools that assist with image upsampling, a critical task in many digital image-related fields
Offers a hands-on, project-based experience that allows learners to immediately apply their knowledge to real-world tasks
Learners must navigate a cloud-based platform, which could be less accessible to those with poor internet connectivity
Save this course
Save Image Super Resolution Using Autoencoders in Keras to your list so you can find it easily later:
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Reviews summary
Practical autoencoder project
This practical project is a hands-on introduction to image super-resolution using autoencoders in Keras. It runs on Coursera's hands-on project platform called Rhyme, which provides a pre-configured cloud desktop with all the necessary software and data. Reviews for this project are mostly positive, with learners appreciating the practical, hands-on approach and the clear explanations by the instructor.
Introduces basics of autoencoders.
"good experience. very clear explanations."
" good experience. very clear explanations. I liked it and recommend it for anyone who wants to understand and experience autoencoder basics. "
Hands-on project with cloud desktop.
"This made autoencoders easier for me to understand"
"Learnt with flow chart of model which made a basis understanding of the project."
"... implement quite well on the platform."
Mixed reviews on Rhyme platform.
"UI of platform was very bad scrolling was very difficult."
"Very lousy implementation and explanation."
"The Rhyme platform is extremely user-unfriendly."
"The course provides a good understanding of the theory. However, the Coursera learning platform is quite unhandy, watching the video and typing at the same time."
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 Image Super Resolution Using Autoencoders in Keras with these
activities:
Review linear algebra and calculus
Show steps
Sharpen your mathematical foundation for a better understanding of autoencoders.
Browse courses on
Linear Algebra
Show steps
-
Review concepts such as vectors, matrices, and linear transformations
-
Practice solving linear equations and matrix operations
-
Refresh your understanding of derivatives and integrals
Practice autoencoder training with different parameters
Show steps
Reinforce your understanding of autoencoders by experimenting with different parameters.
Browse courses on
Autoencoders
Show steps
-
Modify the number of hidden layers in the autoencoder
-
Adjust the number of neurons in the hidden layers
-
Try different activation functions for the hidden layers
-
Experiment with different loss functions
Find a mentor in the field of autoencoders
Show steps
Accelerate your learning by seeking guidance from an experienced professional.
Browse courses on
Autoencoders
Show steps
-
Identify potential mentors who align with your interests
-
Reach out to them and express your interest in mentoring
-
Establish clear expectations and schedule regular meetings
-
Actively engage in discussions and seek feedback
Five other activities
Expand to see all activities and additional details
Show all eight activities
Collaborate on an autoencoder project with a peer
Show steps
Deepen your understanding and practical skills by collaborating on a real-world autoencoder project.
Browse courses on
Autoencoders
Show steps
-
Find a peer with complementary skills and interests
-
Define the scope and objectives of your project
-
Divide the responsibilities and work together to implement the autoencoder
-
Present your results and share your learnings
Create a presentation on autoencoder applications
Show steps
Solidify your understanding of autoencoders by exploring their various applications.
Browse courses on
Autoencoders
Show steps
-
Research different industries and domains where autoencoders are used
-
Choose specific case studies to highlight
-
Design and create slides for your presentation
-
Prepare a compelling narrative to engage your audience
Attend a workshop on autoencoder applications in industry
Show steps
Gain insights into real-world applications of autoencoders and connect with industry experts.
Browse courses on
Autoencoders
Show steps
-
Research and identify relevant workshops in your area
-
Register and attend the workshop
-
Actively participate in discussions and networking opportunities
-
Follow up with speakers and attendees to expand your professional network
Follow tutorials on advanced autoencoder techniques
Show steps
Expand your knowledge and skills by exploring advanced autoencoder techniques.
Browse courses on
Autoencoders
Show steps
-
Identify tutorials on topics such as variational autoencoders or generative adversarial networks
-
Follow the tutorials step-by-step and implement the techniques
-
Experiment with different parameters and configurations
-
Evaluate and compare the results
Contribute to an open-source autoencoder library or project
Show steps
Gain practical experience and contribute to the community by working on an open-source autoencoder project.
Browse courses on
Autoencoders
Show steps
-
Identify a suitable open-source project
-
Familiarize yourself with the codebase and documentation
-
Identify an area where you can contribute
-
Make a pull request with your changes
Review linear algebra and calculus
Show steps
Sharpen your mathematical foundation for a better understanding of autoencoders.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review concepts such as vectors, matrices, and linear transformations
- Practice solving linear equations and matrix operations
- Refresh your understanding of derivatives and integrals
Practice autoencoder training with different parameters
Show steps
Reinforce your understanding of autoencoders by experimenting with different parameters.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Modify the number of hidden layers in the autoencoder
- Adjust the number of neurons in the hidden layers
- Try different activation functions for the hidden layers
- Experiment with different loss functions
Find a mentor in the field of autoencoders
Show steps
Accelerate your learning by seeking guidance from an experienced professional.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Identify potential mentors who align with your interests
- Reach out to them and express your interest in mentoring
- Establish clear expectations and schedule regular meetings
- Actively engage in discussions and seek feedback
Five other activities
Expand to see all activities and additional details
Show all eight activities
Collaborate on an autoencoder project with a peer
Show steps
Deepen your understanding and practical skills by collaborating on a real-world autoencoder project.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Find a peer with complementary skills and interests
- Define the scope and objectives of your project
- Divide the responsibilities and work together to implement the autoencoder
- Present your results and share your learnings
Create a presentation on autoencoder applications
Show steps
Solidify your understanding of autoencoders by exploring their various applications.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Research different industries and domains where autoencoders are used
- Choose specific case studies to highlight
- Design and create slides for your presentation
- Prepare a compelling narrative to engage your audience
Attend a workshop on autoencoder applications in industry
Show steps
Gain insights into real-world applications of autoencoders and connect with industry experts.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Research and identify relevant workshops in your area
- Register and attend the workshop
- Actively participate in discussions and networking opportunities
- Follow up with speakers and attendees to expand your professional network
Follow tutorials on advanced autoencoder techniques
Show steps
Expand your knowledge and skills by exploring advanced autoencoder techniques.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Identify tutorials on topics such as variational autoencoders or generative adversarial networks
- Follow the tutorials step-by-step and implement the techniques
- Experiment with different parameters and configurations
- Evaluate and compare the results
Contribute to an open-source autoencoder library or project
Show steps
Gain practical experience and contribute to the community by working on an open-source autoencoder project.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Identify a suitable open-source project
- Familiarize yourself with the codebase and documentation
- Identify an area where you can contribute
- Make a pull request with your changes
Career center
Learners who complete Image Super Resolution Using Autoencoders in Keras will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and implementing machine learning models. By taking this course, Machine Learning Engineers can develop the skills necessary to use Keras, an open-source neural network library, to enhance images using autoencoders. This course can help Machine Learning Engineers create high-quality images and improve the accuracy of machine learning models.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course provides Data Scientists with the skills necessary to apply machine learning techniques to enhance the quality of data for analysis. With this knowledge and skillset, Data Scientists can build more accurate predictive models and make more informed business decisions.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. This course provides Software Engineers with the skills necessary to use Keras, a popular deep learning library, to develop deep learning models. By taking this course, Software Engineers can gain experience in using autoencoders to enhance images and improve the performance of software systems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop systems that enable computers to interpret, understand, and respond to images and videos. This course may be useful for Computer Vision Engineers who are interested in using autoencoders to enhance the quality of images and improve the performance of computer vision systems.
Data Analyst
Data Analyst
Data Analysts use data to identify trends, patterns, and insights. This course may be useful for Data Analysts who are interested in using autoencoders to enhance the quality of data and improve the accuracy of data analysis.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems and improve decision-making. This course may be useful for Business Analysts who are interested in using autoencoders to enhance the quality of data and improve the accuracy of business analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and features. This course may be useful for Product Managers who are interested in using autoencoders to enhance the quality of images and improve the user experience of products.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course may be useful for Marketing Managers who are interested in using autoencoders to enhance the quality of images and improve the effectiveness of marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. This course may be useful for Sales Managers who are interested in using autoencoders to enhance the quality of images and improve the effectiveness of sales presentations.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring the success of customers. This course may be useful for Customer Success Managers who are interested in using autoencoders to enhance the quality of images and improve the customer experience.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. This course may be useful for Operations Managers who are interested in using autoencoders to enhance the quality of images and improve the efficiency of operations.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course may be useful for Project Managers who are interested in using autoencoders to enhance the quality of images and improve the communication and collaboration of project teams.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. This course may be useful for Financial Analysts who are interested in using autoencoders to enhance the quality of data and improve the accuracy of financial analysis.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for managing the human resources of a company. This course may be useful for Human Resources Managers who are interested in using autoencoders to enhance the quality of images and improve the efficiency of human resources processes.
Technical Writer
Technical Writer
Technical Writers are responsible for creating and maintaining technical documentation. This course may be useful for Technical Writers who are interested in using autoencoders to enhance the quality of images and improve the clarity of technical documentation.
For more career information including salaries, visit:
OpenCourser.com/course/i9tgr7/image
Reading list
We've selected six 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
Image Super Resolution Using Autoencoders in Keras.
Save
Provides a comprehensive overview of deep learning, a type of artificial intelligence that has achieved state-of-the-art results in a wide range of tasks, including image recognition, natural language processing, and speech recognition. It valuable resource for anyone interested in learning more about deep learning and its applications.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), a type of artificial intelligence that can generate realistic images, videos, and other data. It valuable resource for anyone interested in learning more about GANs and their applications.
Provides a practical guide to machine learning, with a focus on deep learning. It is written by Aurélien Géron, a leading researcher in the field of artificial intelligence. The book covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical guide to deep learning, with a focus on using the Python programming language. It is written by François Chollet, the creator of the Keras deep learning library. The book covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical guide to deep learning, with a focus on using the TensorFlow deep learning library. It is written by Bharat Bushan, a leading researcher in the field of artificial intelligence. The book covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Save
Provides a collection of recipes for solving common problems in deep learning. It is written by John Mount, a leading researcher in the field of artificial intelligence. The book covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i9tgr7/image
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Time
1 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Snehan Kekre
Good to know
Develops strong foundations for intermediate learners, who can then move on to more advanced projects
Teaches learners in-demand tools that assist with image upsampling, a critical task in many digital image-related fields
Offers a hands-on, project-based experience that allows learners to immediately apply their knowledge to real-world tasks
Learners must navigate a cloud-based platform, which could be less accessible to those with poor internet connectivity
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