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Emmanuel Acheampong
Welcome to the “Building a Keras Horse Zebra CycleGAN Webapp with Streamlit” guided project.
In this project, we will build a Streamlit web app of a keras trained computer vision CycleGAN for horse images to zebra and vice versa. This project will take a jpg image of a horse and transform it into a zebra and take a picture of a zebra and transform it to a horse.
This project is an intermediate python project for anyone interested in learning about how to productionize computer vision models or more specifically a beginner GAN model with Streamlit and Python.
Read more
Welcome to the “Building a Keras Horse Zebra CycleGAN Webapp with Streamlit” guided project.
In this project, we will build a Streamlit web app of a keras trained computer vision CycleGAN for horse images to zebra and vice versa. This project will take a jpg image of a horse and transform it into a zebra and take a picture of a zebra and transform it to a horse.
This project is an intermediate python project for anyone interested in learning about how to productionize computer vision models or more specifically a beginner GAN model with Streamlit and Python.
Read more
Welcome to the “Building a Keras Horse Zebra CycleGAN Webapp with Streamlit” guided project.
In this project, we will build a Streamlit web app of a keras trained computer vision CycleGAN for horse images to zebra and vice versa. This project will take a jpg image of a horse and transform it into a zebra and take a picture of a zebra and transform it to a horse.
This project is an intermediate python project for anyone interested in learning about how to productionize computer vision models or more specifically a beginner GAN model with Streamlit and Python.
It requires preliminary knowledge on how to build and train GAN models (as we will not be building or training models) but we will be using a model that has already been trained and provided in the workspace.
Register for this course and see more details by visiting:
OpenCourser.com/course/sp4ymk/building
What's inside
Syllabus
Project Overview
Here you will describe what the project is about...give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches how to productionize computer vision models with Streamlit, a useful skill for those interested in a career as a data engineer
Provides an opportunity to enhance python programming skills
Builds knowledge of GAN models for image processing, a fundamental concept in computer vision
Requires students to install specific software, which may cause inconvenience
Assumes learners already have a working knowledge of GAN models and their training process, which may limit accessibility for beginners
Save this course
Save Building a Keras Horse Zebra CycleGAN Webapp with Streamlit to your list so you can find it easily later:
Save
Save
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 Building a Keras Horse Zebra CycleGAN Webapp with Streamlit with these
activities:
Review pre-trained CycleGAN models
Show steps
Prepare your existing knowledge and skills on building and training GAN models before beginning the course.
Browse courses on
CycleGAN
Show steps
-
Review a book or online resource that explains the concepts and architecture of CycleGAN models.
-
Read research papers or articles that explore different applications of CycleGAN models.
-
Build and train a simple CycleGAN model on a small dataset to gain hands-on experience.
Follow tutorials on Streamlit for web development
Show steps
Gain a deeper understanding of the concepts and techniques used in building Streamlit web applications.
Browse courses on
Streamlit
Show steps
-
Complete the official Streamlit documentation tutorial.
-
Follow a YouTube tutorial or online course that provides a comprehensive overview of Streamlit.
-
Build a simple Streamlit app that demonstrates the basics of interactivity and data visualization.
Complete coding exercises on data preprocessing and image augmentation
Show steps
Reinforce your understanding of the data manipulation and enhancement techniques used in the course.
Browse courses on
Data Preprocessing
Show steps
-
Solve coding problems on platforms like LeetCode or Hackerrank.
-
Implement data preprocessing and augmentation techniques in your own Python scripts.
-
Analyze the impact of different preprocessing and augmentation techniques on model performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a blog post or article about CycleGANs and Streamlit
Show steps
Deepen your understanding of the concepts by explaining them to others and solidifying your knowledge.
Browse courses on
Streamlit
Show steps
-
Write a clear and engaging blog post or article.
-
Research and gather information about CycleGANs and Streamlit.
-
Outline the key points and concepts you want to cover.
-
Publish your content on a relevant platform.
Develop a personal portfolio website with Streamlit
Show steps
Apply the skills learned in the course by creating a tangible project that showcases your abilities.
Browse courses on
Portfolio Website
Show steps
-
Design the layout and structure of your portfolio website.
-
Implement interactive features and data visualizations using Streamlit.
-
Showcase your projects, skills, and experience on the website.
-
Deploy your website to a hosting platform.
Participate in a Kaggle competition related to image generation or data visualization
Show steps
Challenge yourself and apply your skills in a real-world setting, fostering both growth and motivation.
Browse courses on
Kaggle
Show steps
-
Identify a relevant Kaggle competition that aligns with your interests and skill level.
-
Study the competition rules and data.
-
Develop and implement your solution.
-
Submit your results and track your progress.
Contribute to an open-source project related to CycleGANs or Streamlit
Show steps
Gain valuable experience collaborating with others and contribute to the broader tech community.
Browse courses on
Open Source
Show steps
-
Identify a relevant open-source project on platforms like GitHub or GitLab.
-
Review the project's documentation and code.
-
Identify areas where you can contribute.
-
Submit a pull request with your contributions.
Review pre-trained CycleGAN models
Show steps
Prepare your existing knowledge and skills on building and training GAN models before beginning the course.
Browse courses on
CycleGAN
Show steps
Show steps
Show steps
- Review a book or online resource that explains the concepts and architecture of CycleGAN models.
- Read research papers or articles that explore different applications of CycleGAN models.
- Build and train a simple CycleGAN model on a small dataset to gain hands-on experience.
Follow tutorials on Streamlit for web development
Show steps
Gain a deeper understanding of the concepts and techniques used in building Streamlit web applications.
Browse courses on
Streamlit
Show steps
Show steps
Show steps
- Complete the official Streamlit documentation tutorial.
- Follow a YouTube tutorial or online course that provides a comprehensive overview of Streamlit.
- Build a simple Streamlit app that demonstrates the basics of interactivity and data visualization.
Complete coding exercises on data preprocessing and image augmentation
Show steps
Reinforce your understanding of the data manipulation and enhancement techniques used in the course.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Solve coding problems on platforms like LeetCode or Hackerrank.
- Implement data preprocessing and augmentation techniques in your own Python scripts.
- Analyze the impact of different preprocessing and augmentation techniques on model performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a blog post or article about CycleGANs and Streamlit
Show steps
Deepen your understanding of the concepts by explaining them to others and solidifying your knowledge.
Browse courses on
Streamlit
Show steps
Show steps
Show steps
- Write a clear and engaging blog post or article.
- Research and gather information about CycleGANs and Streamlit.
- Outline the key points and concepts you want to cover.
- Publish your content on a relevant platform.
Develop a personal portfolio website with Streamlit
Show steps
Apply the skills learned in the course by creating a tangible project that showcases your abilities.
Browse courses on
Portfolio Website
Show steps
Show steps
Show steps
- Design the layout and structure of your portfolio website.
- Implement interactive features and data visualizations using Streamlit.
- Showcase your projects, skills, and experience on the website.
- Deploy your website to a hosting platform.
Participate in a Kaggle competition related to image generation or data visualization
Show steps
Challenge yourself and apply your skills in a real-world setting, fostering both growth and motivation.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify a relevant Kaggle competition that aligns with your interests and skill level.
- Study the competition rules and data.
- Develop and implement your solution.
- Submit your results and track your progress.
Contribute to an open-source project related to CycleGANs or Streamlit
Show steps
Gain valuable experience collaborating with others and contribute to the broader tech community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify a relevant open-source project on platforms like GitHub or GitLab.
- Review the project's documentation and code.
- Identify areas where you can contribute.
- Submit a pull request with your contributions.
Career center
Learners who complete Building a Keras Horse Zebra CycleGAN Webapp with Streamlit will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists may find this course useful. This course can help provide insights into deep learning architectures that are used to create robust machine learning algorithms. The course also covers how to implement these algorithms using Python and Streamlit, which are valuable skills for data scientists.
Machine Learning Engineer
Machine Learning Engineer
Taking this course may be constructive for Machine Learning Engineers. This course provides hands-on experience in building and deploying a machine learning model using Keras, which is a popular deep learning library.
Software Engineer
Software Engineer
This course can be helpful for Software Engineers who want to specialize in deep learning. The course covers the fundamentals of deep learning and how to apply them to real-world problems. The course also provides hands-on experience in building and deploying a deep learning model using Keras and Streamlit.
Deep Learning Engineer
Deep Learning Engineer
This course may be beneficial for Deep Learning Engineers. The course provides a deep dive into deep learning architectures and their applications. The course also provides hands-on experience in building and deploying a deep learning model using Keras and Streamlit.
Computer Vision Engineer
Computer Vision Engineer
This course can be helpful for Computer Vision Engineers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to computer vision problems. The course also provides hands-on experience in building and deploying a deep learning model using Keras and Streamlit.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
This course may be helpful for Artificial Intelligence Engineers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to artificial intelligence problems.
Data Analyst
Data Analyst
Taking this course may be helpful for Data Analysts who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to data analysis problems.
Quantitative Analyst
Quantitative Analyst
This course may be helpful for Quantitative Analysts who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to quantitative analysis problems.
Business Analyst
Business Analyst
This course can be useful for Business Analysts who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to business analysis problems.
Product Manager
Product Manager
Taking this course may be helpful for Product Managers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to product management problems.
Project Manager
Project Manager
This course can be useful for Project Managers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to project management problems.
Marketing Manager
Marketing Manager
Taking this course may be helpful for Marketing Managers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to marketing problems.
Sales Manager
Sales Manager
This course may be helpful for Sales Managers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to sales problems.
Customer Success Manager
Customer Success Manager
Taking this course may be helpful for Customer Success Managers who want to learn about deep learning. The course covers the fundamentals of deep learning and how to apply them to customer success problems.
For more career information including salaries, visit:
OpenCourser.com/course/sp4ymk/building
Reading list
We've selected ten 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
Building a Keras Horse Zebra CycleGAN Webapp with Streamlit .
Provides a comprehensive overview of algorithm design and analysis techniques. It valuable resource for anyone interested in learning more about algorithms.
Provides a comprehensive overview of computer vision techniques using Python. It valuable resource for anyone interested in learning more about computer vision.
Provides a comprehensive overview of deep learning techniques using Python. It valuable resource for anyone interested in learning more about deep learning.
Save
Provides a comprehensive overview of GANs, their history, theory, and applications. It valuable resource for anyone interested in learning more about GANs.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It valuable resource for anyone interested in learning more about computer vision.
Provides a comprehensive overview of digital image processing techniques. It valuable resource for anyone interested in learning more about digital image processing.
Provides a comprehensive overview of Python for data analysis. It valuable resource for anyone interested in learning more about using Python for data analysis.
Save
Provides a comprehensive overview of deep learning concepts. It valuable resource for anyone interested in learning more about deep learning.
Provides a concise overview of computer science concepts. It valuable resource for anyone interested in learning more about computer science.
Provides a comprehensive overview of the Keras deep learning library. It valuable resource for anyone interested in using Keras to build deep learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sp4ymk/building
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Emmanuel Acheampong
Language
English
Good to know
Teaches how to productionize computer vision models with Streamlit, a useful skill for those interested in a career as a data engineer
Provides an opportunity to enhance python programming skills
Builds knowledge of GAN models for image processing, a fundamental concept in computer vision
Requires students to install specific software, which may cause inconvenience
Assumes learners already have a working knowledge of GAN models and their training process, which may limit accessibility for beginners
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