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Ari Anastassiou
In this 1-hour long project, you will be introduced to the Variational Autoencoder. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. Our data comprises 60.000 characters from a dataset of fonts. We will train a variational autoencoder that will be capable of compressing this character font data from 2500 dimensions down to 32 dimensions. This same model will be able to then reconstruct its original input with high fidelity. The true advantage of the variational autoencoder is its ability to create new outputs that come from distributions that closely follow its training data: we can output characters in brand new fonts.
Read more
In this 1-hour long project, you will be introduced to the Variational Autoencoder. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. Our data comprises 60.000 characters from a dataset of fonts. We will train a variational autoencoder that will be capable of compressing this character font data from 2500 dimensions down to 32 dimensions. This same model will be able to then reconstruct its original input with high fidelity. The true advantage of the variational autoencoder is its ability to create new outputs that come from distributions that closely follow its training data: we can output characters in brand new fonts.
Read more
In this 1-hour long project, you will be introduced to the Variational Autoencoder. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. Our data comprises 60.000 characters from a dataset of fonts. We will train a variational autoencoder that will be capable of compressing this character font data from 2500 dimensions down to 32 dimensions. This same model will be able to then reconstruct its original input with high fidelity. The true advantage of the variational autoencoder is its ability to create new outputs that come from distributions that closely follow its training data: we can output characters in brand new fonts.
Note: 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/d7lw8x/image
What's inside
Syllabus
Image Compression and Generation using Variational Autoencoders in Python
This 1.5-hour project will introduce the Variational Autoencoder. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. Our data comprises 60.000 characters from a dataset of fonts. We will train a variational autoencoder that will be capable of compressing this character font data from 2500 dimensions down to 32 dimensions. This same model will be able to then reconstruct its original input with high fidelity. The true advantage of the variational autoencoder is its ability to create new outputs that come from distributions that closely follow its training data: we can output characters in brand new fonts.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps students develop skills in image compression and generation with variational autoencoders, which are highly relevant to machine learning and artificial intelligence
Provides students with an introduction to the fundamental concepts of variational autoencoders, enabling them to gain a strong foundation in this advanced machine learning technique
Engages students in hands-on learning through a practical project involving the implementation of a variational autoencoder for font character compression and reconstruction
Implements a creative approach to teaching variational autoencoders, allowing students to explore the model's capabilities in generating new and diverse font characters
Emphasizes the utilization of Python, a widely adopted programming language in machine learning and data science, enhancing the course's relevance and practicality
Requires students to have basic knowledge of deep learning and neural networks, which may limit accessibility for beginners in the field
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Reviews summary
Popular course for vae in pytorch
Learners say this course is well received especially those new to PyTorch who want to apply Variational Autoencoders (VAEs) with Python. Many learners found the assignments engaging, even those with a background in coding and VAEs. The majority of students rated this course positively with 11 out of the 13 reviews being 5-stars. While there are limited reviews to work with, the only concern is that some learners recommend having a strong background in ML before taking this course.
Course is better for learners new to PyTorch
"Thank you "
"It was really helpful. I am new to PyTorch but it gave a good level of understanding overall. thank you"
Course provides engaging assignments
"Good Initiation.."
"Good project. Add some more clarity to it , especially to the mathematical background."
"It is highly recommended to those who has a basic knowledge in ML and like to start using VAEs in pytorch framework. :-)"
Requires strong background in ML
"Just fine for someone who has enough idea on coding as well as some idea on VAE"
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 Compression and Generation using Variational Autoencoders in Python with these
activities:
Read 'Deep Learning with Python'
Show steps
This book provides essential background knowledge on the theory and implementation of Deep Learning methods, making it an ideal companion to the course content.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read chapters 1-3 to understand the foundations of Deep Learning.
-
Work through the exercises in chapter 4 to gain hands-on experience with building Deep Learning models.
Follow a tutorial on implementing Variational Autoencoders using Tensorflow
Show steps
Following a guided tutorial will provide you with step-by-step instructions and examples to reinforce your learning.
Browse courses on
Variational Autoencoders
Show steps
-
Find a tutorial on implementing Variational Autoencoders using Tensorflow.
-
Follow the tutorial and complete the exercises.
Join a study group for Variational Autoencoders
Show steps
Discussing concepts and working on problems with peers can enhance your understanding and retention.
Browse courses on
Variational Autoencoders
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course material and work on projects.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve coding challenges on Variational Autoencoders
Show steps
Regular practice with coding challenges will significantly improve your proficiency in implementing Variational Autoencoders.
Browse courses on
Variational Autoencoders
Show steps
-
Find online coding challenges related to Variational Autoencoders.
-
Attempt to solve the challenges and debug your code.
Design a Variational Autoencoder architecture
Show steps
Designing your own Variational Autoencoder architecture will reinforce your understanding of its components and functionality.
Browse courses on
Variational Autoencoders
Show steps
-
Sketch out a basic Variational Autoencoder architecture on paper.
-
Implement the architecture in Python using a deep learning library.
Build a Variational Autoencoder for character generation
Show steps
Building a Variational Autoencoder for character generation will showcase your skills in applying the model to a specific problem.
Browse courses on
Variational Autoencoders
Show steps
-
Gather a dataset of character fonts.
-
Train a Variational Autoencoder on the dataset.
-
Use the trained model to generate new character fonts.
Read 'Deep Learning with Python'
Show steps
This book provides essential background knowledge on the theory and implementation of Deep Learning methods, making it an ideal companion to the course content.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the foundations of Deep Learning.
- Work through the exercises in chapter 4 to gain hands-on experience with building Deep Learning models.
Follow a tutorial on implementing Variational Autoencoders using Tensorflow
Show steps
Following a guided tutorial will provide you with step-by-step instructions and examples to reinforce your learning.
Browse courses on
Variational Autoencoders
Show steps
Show steps
Show steps
- Find a tutorial on implementing Variational Autoencoders using Tensorflow.
- Follow the tutorial and complete the exercises.
Join a study group for Variational Autoencoders
Show steps
Discussing concepts and working on problems with peers can enhance your understanding and retention.
Browse courses on
Variational Autoencoders
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course material and work on projects.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve coding challenges on Variational Autoencoders
Show steps
Regular practice with coding challenges will significantly improve your proficiency in implementing Variational Autoencoders.
Browse courses on
Variational Autoencoders
Show steps
Show steps
Show steps
- Find online coding challenges related to Variational Autoencoders.
- Attempt to solve the challenges and debug your code.
Design a Variational Autoencoder architecture
Show steps
Designing your own Variational Autoencoder architecture will reinforce your understanding of its components and functionality.
Browse courses on
Variational Autoencoders
Show steps
Show steps
Show steps
- Sketch out a basic Variational Autoencoder architecture on paper.
- Implement the architecture in Python using a deep learning library.
Build a Variational Autoencoder for character generation
Show steps
Building a Variational Autoencoder for character generation will showcase your skills in applying the model to a specific problem.
Browse courses on
Variational Autoencoders
Show steps
Show steps
Show steps
- Gather a dataset of character fonts.
- Train a Variational Autoencoder on the dataset.
- Use the trained model to generate new character fonts.
Career center
Learners who complete Image Compression and Generation using Variational Autoencoders in Python will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. These systems enable computers to see and interpret images and videos. This course in Image Compression and Generation using Variational Autoencoders in Python will help you build a foundation in computer vision with a focus on image compression and generation. You'll learn how to use Variational Autoencoders to compress images and generate new images, which are essential skills for Computer Vision Engineers. These skills are in high demand as computer vision is used in a wide range of applications, from self-driving cars to medical diagnosis.
Research Scientist in Machine Learning
Research Scientist in Machine Learning
Research Scientists in Machine Learning conduct research on new machine learning algorithms and techniques. They develop new methods for solving complex problems in a variety of fields, from healthcare to finance. This course in Image Compression and Generation using Variational Autoencoders in Python will help you build a strong foundation in machine learning research. You'll learn about the latest techniques in image compression and generation, as well as how to design and conduct research experiments. These skills will make you a competitive candidate for Research Scientist positions, where you can contribute to the advancement of machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. They work closely with Data Scientists to ensure that models are implemented efficiently and effectively. This course in Image Compression and Generation using Variational Autoencoders in Python will provide you with a strong foundation in machine learning, as well as experience with the latest techniques in image compression and generation. You'll learn how to use Variational Autoencoders to compress images without losing important information, and how to generate new images from a given dataset. These skills are in high demand for Machine Learning Engineers, as they enable them to build more efficient and effective machine learning models.
Data Scientist
Data Scientist
Data Scientists use machine learning, modeling, and data analysis to solve complex problems. They provide valuable insights for businesses that help make better decisions. This course in Image Compression and Generation using Variational Autoencoders in Python will help you develop the necessary skills in machine learning and data analysis. This course provides you with hands-on experience with Variational Autoencoders, a powerful technique for unsupervised learning. You'll learn how to apply this technique to compress images and generate new images from a given dataset. These skills will open up opportunities for you as a Data Scientist, helping you analyze and make sense of large datasets more effectively.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. They use a variety of tools and techniques to extract insights from data, including machine learning and data mining. This course in Image Compression and Generation using Variational Autoencoders in Python will help you develop the skills you need to be a successful Data Analyst. You'll learn how to use Variational Autoencoders to compress and generate images, which are valuable skills for Data Analysts who work with image data. These skills will help you extract more insights from data and make better recommendations to businesses.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a variety of projects, from mobile apps to enterprise software. This course in Image Compression and Generation using Variational Autoencoders in Python will help you build a foundation in software engineering, with a focus on image compression and generation. You'll learn how to use Variational Autoencoders to compress and generate images, which are valuable skills for Software Engineers who work on image-related projects. These skills will help you develop more efficient and effective software solutions.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work closely with engineers, designers, and marketers to ensure that products meet the needs of customers. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Product Managers who work on image-related products. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more innovative and user-friendly products.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They help businesses make informed investment decisions. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Quantitative Analysts who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more accurate and reliable financial models.
Business Analyst
Business Analyst
Business Analysts help businesses improve their operations. They use data analysis and modeling to identify problems and develop solutions. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Business Analysts who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more effective solutions to business problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. They help businesses optimize their operations and make better decisions. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Operations Research Analysts who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more efficient and effective solutions to business problems.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of fields, including healthcare, finance, and marketing. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Statisticians who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more accurate and reliable statistical models.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They work with individuals and institutions to help them manage their finances. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Financial Analysts who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more accurate and reliable financial models.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and trends. They help businesses understand their customers and develop new products and services. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Market Researchers who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you develop more effective marketing campaigns.
User Experience Designer
User Experience Designer
User Experience Designers design and develop the user interface for websites and apps. They focus on creating a positive and intuitive user experience. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for User Experience Designers who work with image-heavy websites or apps. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you create more visually appealing and user-friendly designs.
Graphic designer
Graphic designer
Graphic Designers create visual concepts to communicate ideas. They work on a variety of projects, from logos and brochures to websites and social media campaigns. This course in Image Compression and Generation using Variational Autoencoders in Python may be useful for Graphic Designers who work with image data. You'll learn how to use Variational Autoencoders to compress and generate images, which can help you create more visually appealing and engaging designs.
For more career information including salaries, visit:
OpenCourser.com/course/d7lw8x/image
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
Image Compression and Generation using Variational Autoencoders in Python.
This paper introduces the variational autoencoder (VAE), a generative model that learns a low-dimensional latent representation of data. VAEs are a powerful tool for image compression, generation, and denoising.
Save
Provides a comprehensive overview of deep learning, including the theory and practice of training and deploying deep neural networks. It valuable resource for anyone who wants to learn more about deep learning and its applications.
Provides a comprehensive overview of deep learning, including the theory and practice of training and deploying deep neural networks. It valuable resource for anyone who wants to learn more about deep learning and its applications.
Provides a comprehensive overview of generative deep learning, including the theory and practice of training and deploying generative adversarial networks (GANs). It valuable resource for anyone who wants to learn more about generative deep learning and its applications.
Provides a practical introduction to deep learning using the Fastai library. It valuable resource for anyone who wants to learn how to train and deploy deep learning models with Fastai.
Provides a practical introduction to machine learning using the Scikit-Learn, Keras, and TensorFlow libraries. It valuable resource for anyone who wants to learn how to train and deploy machine learning models with these libraries.
Provides a comprehensive overview of machine learning using the Python programming language. Python popular language for machine learning because of its ease of use and its wide range of libraries.
Provides a practical introduction to machine learning for people who have no prior experience with the subject. This book valuable resource for anyone who wants to get started with machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. This book valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. This book valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning, a type of machine learning that uses statistical methods to learn from data. This book valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning, a type of machine learning that uses statistical methods to learn from data. This book valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d7lw8x/image
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Effort
90 minutes
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
Helps students develop skills in image compression and generation with variational autoencoders, which are highly relevant to machine learning and artificial intelligence
Provides students with an introduction to the fundamental concepts of variational autoencoders, enabling them to gain a strong foundation in this advanced machine learning technique
Engages students in hands-on learning through a practical project involving the implementation of a variational autoencoder for font character compression and reconstruction
Implements a creative approach to teaching variational autoencoders, allowing students to explore the model's capabilities in generating new and diverse font characters
Emphasizes the utilization of Python, a widely adopted programming language in machine learning and data science, enhancing the course's relevance and practicality
Requires students to have basic knowledge of deep learning and neural networks, which may limit accessibility for beginners in the field
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