We may earn an affiliate commission when you visit our partners.
Course image
Emmanuel Acheampong

Welcome to the “Building a Keras Horse Zebra CycleGAN Webapp with Streamlit” guided project.

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.

Enroll now

Two deals to help you save

We found two deals and offers that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

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

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

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
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
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
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
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
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
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
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.

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 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
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
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
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
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
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
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
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
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
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
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
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
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
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.

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.
Provides a comprehensive overview of GANs, their history, theory, and applications. It valuable resource for anyone interested in learning more about GANs.
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.
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.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Building a Keras Horse Zebra CycleGAN Webapp with Streamlit .
Image Colorization using TensorFlow 2 and Keras
Most relevant
Hand Gesture Recognition using Tensorflow and Keras
Most relevant
Deploy an NLP Text Generator: Bart Simpson Chalkboard Gag
Most relevant
Deploy Bridgerton NLP SMS Text Generator
Most relevant
Deploying a Pytorch Computer Vision Model API to Heroku
Most relevant
Generate Synthetic Images with DCGANs in Keras
Most relevant
Creating Multi Task Models With Keras
Most relevant
Activity Recognition using Python, Tensorflow and Keras
Most relevant
GenAI Summarization with Langchain: Summarize Text...
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser