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Snehan Kekre
In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. We will be using the Keras Sequential API with Tensorflow 2 as the backend.
Read more
In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. We will be using the Keras Sequential API with Tensorflow 2 as the backend.
Read more
In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. We will be using the Keras Sequential API with Tensorflow 2 as the backend.
In our GAN setup, we want to be able to sample from a complex, high-dimensional training distribution of the Fashion MNIST images. However, there is no direct way to sample from this distribution. The solution is to sample from a simpler distribution, such as Gaussian noise. We want the model to use the power of neural networks to learn a transformation from the simple distribution directly to the training distribution that we care about. The GAN consists of two adversarial players: a discriminator and a generator. We’re going to train the two players jointly in a minimax game theoretic formulation.
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/t7d5gz/generate
What's inside
Syllabus
Project: Generate Synthetic Images with DCGANs in Keras
In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. We will be using the Keras Sequential API with Tensorflow 2 as the backend.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Developers knowledge of Generative Adversarial Networks (GANs) and ability to build and train a Deep Convolutional GAN (DCGAN) with Keras
Utilises Keras Sequential API with Tensorflow 2 as the backend, which are industry-standard tools
Provides hands-on experience through Rhyme, a platform designed for practical learning
Suitable for learners based in the North America region, with efforts underway to expand accessibility
Limited access to the cloud desktop (5 times)
Save this course
Save Generate Synthetic Images with DCGANs in Keras to your list so you can find it easily later:
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Reviews summary
Well-received gan course
Learners say this course is a solid introduction to Generative Adversarial Networks (GANs), especially for those interested in implementing them in Keras. According to students, the course offers good, clear instruction, and a well-prepared curriculum. Learners also praise the instructor for being great and excellent.
Instructor is great
"Excellent instructor. Dense with content and comments explaining bits of code."
"Trainer was awesome "
"Great Course, Learned a lot. Thanks Snehan."
Engaging and useful projects
"Mostly likeable project & good"
"It was a great experience with the Guided projects"
"Quick and easy to follow, very informative as well!"
Good starting point
"It's a very good start to know more about GANs"
"A good basic understanding of DCGANS."
"Nice choice to start with the understanding of GANs."
Clear and concise instruction
"Very clear and concise instructions"
"Good way to start out implementing DCGANS!!"
"Very well explanation towards completion of the code."
Some room for improvements
"Still room for a lot of improvements, average material"
"I would choose to learn online rather than study this course. The course was not well-prepared."
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 Generate Synthetic Images with DCGANs in Keras with these
activities:
Organize your course materials
Show steps
Keep your course materials organized for easy reference.
Show steps
-
Create a folder for your course materials.
-
Save all of your course materials in the folder.
-
Create a system for organizing your materials.
Review Generative Adversarial Networks by Ian Goodfellow
Show steps
Familiarize yourself with the theoretical foundations of GANs.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Summarize the key concepts of GANs.
-
Identify the strengths and weaknesses of GANs.
Follow a tutorial on how to build a DCGAN in Keras
Show steps
Gain practical experience in building and training DCGANs.
Show steps
-
Find a tutorial on how to build a DCGAN in Keras.
-
Follow the tutorial step-by-step.
-
Modify the tutorial to train a DCGAN on your own dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice coding GANs in Keras
Show steps
Solidify your understanding of GANs by coding them yourself.
Show steps
-
Set up a Python environment with Keras installed.
-
Code a simple DCGAN in Keras.
-
Train the DCGAN on your own dataset.
-
Evaluate the performance of the DCGAN.
Create a blog post or video tutorial on how to build a DCGAN in Keras
Show steps
Deepen your understanding of GANs by teaching others how to build them.
Show steps
-
Choose a topic for your blog post or video tutorial.
-
Write or record your content.
-
Publish your content online.
Mentor other students in the course
Show steps
Reinforce your knowledge by helping others learn.
Show steps
-
Join the course discussion forum.
-
Answer questions from other students.
-
Provide feedback on other students' work.
Contribute to an open-source GAN project
Show steps
Apply your skills to a real-world project and gain experience working with others.
Show steps
-
Find an open-source GAN project to contribute to.
-
Identify an area where you can contribute.
-
Submit a pull request with your contribution.
Organize your course materials
Show steps
Keep your course materials organized for easy reference.
Show steps
Show steps
Show steps
- Create a folder for your course materials.
- Save all of your course materials in the folder.
- Create a system for organizing your materials.
Review Generative Adversarial Networks by Ian Goodfellow
Show steps
Familiarize yourself with the theoretical foundations of GANs.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Summarize the key concepts of GANs.
- Identify the strengths and weaknesses of GANs.
Follow a tutorial on how to build a DCGAN in Keras
Show steps
Gain practical experience in building and training DCGANs.
Show steps
Show steps
Show steps
- Find a tutorial on how to build a DCGAN in Keras.
- Follow the tutorial step-by-step.
- Modify the tutorial to train a DCGAN on your own dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice coding GANs in Keras
Show steps
Solidify your understanding of GANs by coding them yourself.
Show steps
Show steps
Show steps
- Set up a Python environment with Keras installed.
- Code a simple DCGAN in Keras.
- Train the DCGAN on your own dataset.
- Evaluate the performance of the DCGAN.
Create a blog post or video tutorial on how to build a DCGAN in Keras
Show steps
Deepen your understanding of GANs by teaching others how to build them.
Show steps
Show steps
Show steps
- Choose a topic for your blog post or video tutorial.
- Write or record your content.
- Publish your content online.
Mentor other students in the course
Show steps
Reinforce your knowledge by helping others learn.
Show steps
Show steps
Show steps
- Join the course discussion forum.
- Answer questions from other students.
- Provide feedback on other students' work.
Contribute to an open-source GAN project
Show steps
Apply your skills to a real-world project and gain experience working with others.
Show steps
Show steps
Show steps
- Find an open-source GAN project to contribute to.
- Identify an area where you can contribute.
- Submit a pull request with your contribution.
Career center
Learners who complete Generate Synthetic Images with DCGANs in Keras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are increasingly in demand as companies of all sizes realize the value of using data to drive decision-making and product development. This course will provide you with the knowledge and skills you need to enter the field of Data Science, and to build and manage a DCGAN. While this course does not cover all advanced topics in Data Science, taking it will put you in a great position to pursue a career in this field. As a professional Data Scientist, you can expect to hold a great deal of responsibility and impact on your company's product and strategy.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers work to solve real-world problems using machine learning techniques. This course will help you build a foundation in Generative Adversarial Networks, which are a type of machine learning model. By completing this course, you will be well on your way to a successful career as a Machine Learning Engineer.
AI Researcher
AI Researcher
AI Researchers work to develop new AI technologies and techniques. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of AI model. By taking this course, you will be well on your way to a successful career as an AI Researcher.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Data Analyst.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. This course will help you build a strong foundation in Generative Adversarial Networks, which are a type of deep learning model. By taking this course, you will be well on your way to a successful career as a Software Engineer.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching new products. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Product Manager.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data systems. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Data Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and using mathematical and statistical models to analyze financial data. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Quantitative Analyst.
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risks. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as an Actuary.
Computer Scientist
Computer Scientist
Computer Scientists are responsible for designing and developing computer systems. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Computer Scientist.
Software Developer
Software Developer
Software Developers are responsible for designing, coding, and testing software. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Software Developer.
Data Science Manager
Data Science Manager
Data Science Managers are responsible for leading teams of data scientists and overseeing data science projects. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Data Science Manager.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Statistician.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and making recommendations for improvements. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Business Analyst.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to a business. This course will provide you with a strong foundation in Generative Adversarial Networks, which are a type of deep learning model that can be used to generate synthetic data. By taking this course, you will be well on your way to a successful career as a Risk Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/t7d5gz/generate
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
Generate Synthetic Images with DCGANs in Keras.
Provides a great overview of generative deep learning, including GANs, and is written in an accessible style.
Provides a comprehensive introduction to deep learning, covering the fundamentals of neural networks, training techniques, and applications. It valuable resource for anyone looking to learn more about deep learning and how to use it in practice.
Save
This paper provides a comprehensive overview of GANs, covering their history, theory, and applications. It valuable resource for anyone looking to learn more about the theoretical foundations of GANs.
Provides a practical guide to using TensorFlow 2.0, which is the framework that will be used in the course.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model training, and evaluation. It valuable resource for anyone looking to learn more about machine learning and how to use it in practice.
Provides a comprehensive guide to TensorFlow 2.0, covering its features, APIs, and applications. It valuable resource for anyone looking to learn more about TensorFlow 2.0 and how to use it in practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t7d5gz/generate
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Effort
1.5 hours
Level
Advanced
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Developers knowledge of Generative Adversarial Networks (GANs) and ability to build and train a Deep Convolutional GAN (DCGAN) with Keras
Utilises Keras Sequential API with Tensorflow 2 as the backend, which are industry-standard tools
Provides hands-on experience through Rhyme, a platform designed for practical learning
Suitable for learners based in the North America region, with efforts underway to expand accessibility
Limited access to the cloud desktop (5 times)
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