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Parth Dhameliya
In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images.
Read more
In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images.
Read more
In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images.
In order to be successful in this project, you will need to have a theoretical understanding on convolutional neural network and optimization algorithm like Adam or gradient descent. This project will focus more on the practical aspect of DCGAN and less on theoretical aspect.
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/ogb9sb/deep
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Syllabus
Project Overview
In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on implementing Deep Convolutional Generative Adversarial Networks (DCGANs) in PyTorch to develop handwritten digits
Builds a foundation in implementing and comprehending DCGAN architectures for real-world applications
Utilizes PyTorch, a respected deep learning framework, for implementing and training DCGANs
Provides practical knowledge and hands-on experience in building DCGANs for generating realistic handwritten digits
Suitable for learners familiar with convolutional neural networks and optimization algorithms
Course content focuses primarily on the practical aspects of DCGAN implementation, with less emphasis on theoretical concepts
Save this course
Save Deep Learning with PyTorch : Generative Adversarial Network to your list so you can find it easily later:
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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 Deep Learning with PyTorch : Generative Adversarial Network with these
activities:
Review Convolutional Neural Networks
Show steps
Ensures a solid foundation in CNNs, which are essential for understanding DCGAN.
Show steps
-
Go through lecture notes or online resources to recap the concepts of CNNs.
-
Practice implementing simple CNN architectures in PyTorch or other frameworks.
-
Review the mathematical operations behind CNNs, such as convolutions, pooling, and activation functions.
Read 'Generative Adversarial Networks' by Ian Goodfellow
Show steps
Provides a comprehensive overview of GANs, including DCGAN.
View
Four Laws for the Artificially Intelligent
on Amazon
Show steps
-
Read the introductory chapters to gain a foundational understanding of GANs.
-
Focus on the chapters dedicated to DCGAN and its applications.
-
Review the code examples and illustrations provided in the book.
Use Deep Convolutional Generative Adversarial Network to Generate Handwritten Digits
Show steps
Helps lay the foundation for understanding DCGAN.
Show steps
-
Set up the environment by installing PyTorch and other required libraries.
-
Create a new project in PyTorch, and write the code for the generator and the discriminator.
-
Load the MNIST dataset.
-
Train the DCGAN model.
-
Evaluate the model's performance and generate handwritten digits.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a Hands-on DCGAN Workshop
Show steps
Offers a structured and interactive learning experience.
Show steps
-
Identify and register for a reputable DCGAN workshop that aligns with your learning goals.
-
Prepare for the workshop by reviewing the provided materials and completing any prerequisites.
-
Actively participate in the workshop sessions, ask questions, and engage with the instructors.
-
Complete the hands-on exercises and projects during the workshop.
-
Follow up after the workshop by practicing the techniques learned and seeking additional resources.
Practice Implementing DCGAN in PyTorch
Show steps
Provides hands-on experience with implementing DCGAN.
Show steps
-
Review the provided code and understand the implementation of the generator and discriminator networks.
-
Modify the code to experiment with different hyperparameters, such as the number of layers, the number of filters, and the learning rate.
-
Create custom data loaders to load your own dataset of images.
-
Visualize the generated images to evaluate the performance of the model.
-
Compare the performance of different DCGAN architectures and hyperparameter settings.
Build a DCGAN Model to Generate Synthetic Images
Show steps
Demonstrates the application of DCGAN in generating synthetic images.
Show steps
-
Choose a specific domain or application for generating synthetic images, such as generating faces, landscapes, or medical images.
-
Collect and preprocess a dataset of real images from the chosen domain.
-
Design and implement a DCGAN model tailored to the specific domain.
-
Train the DCGAN model on the collected dataset.
-
Evaluate the performance of the model and compare it with existing methods.
Contribute to an Open-Source DCGAN Project
Show steps
Provides real-world experience and exposure to best practices.
Show steps
-
Identify an open-source DCGAN project that aligns with your interests and skills.
-
Familiarize yourself with the project's codebase and documentation.
-
Propose and implement a new feature or improvement to the project.
-
Contribute your changes back to the project and engage with the community.
-
Review and provide feedback on contributions from other developers.
Review Convolutional Neural Networks
Show steps
Ensures a solid foundation in CNNs, which are essential for understanding DCGAN.
Show steps
Show steps
Show steps
- Go through lecture notes or online resources to recap the concepts of CNNs.
- Practice implementing simple CNN architectures in PyTorch or other frameworks.
- Review the mathematical operations behind CNNs, such as convolutions, pooling, and activation functions.
Read 'Generative Adversarial Networks' by Ian Goodfellow
Show steps
Provides a comprehensive overview of GANs, including DCGAN.
View
Four Laws for the Artificially Intelligent
on Amazon
Show steps
Show steps
Show steps
- Read the introductory chapters to gain a foundational understanding of GANs.
- Focus on the chapters dedicated to DCGAN and its applications.
- Review the code examples and illustrations provided in the book.
Use Deep Convolutional Generative Adversarial Network to Generate Handwritten Digits
Show steps
Helps lay the foundation for understanding DCGAN.
Show steps
Show steps
Show steps
- Set up the environment by installing PyTorch and other required libraries.
- Create a new project in PyTorch, and write the code for the generator and the discriminator.
- Load the MNIST dataset.
- Train the DCGAN model.
- Evaluate the model's performance and generate handwritten digits.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a Hands-on DCGAN Workshop
Show steps
Offers a structured and interactive learning experience.
Show steps
Show steps
Show steps
- Identify and register for a reputable DCGAN workshop that aligns with your learning goals.
- Prepare for the workshop by reviewing the provided materials and completing any prerequisites.
- Actively participate in the workshop sessions, ask questions, and engage with the instructors.
- Complete the hands-on exercises and projects during the workshop.
- Follow up after the workshop by practicing the techniques learned and seeking additional resources.
Practice Implementing DCGAN in PyTorch
Show steps
Provides hands-on experience with implementing DCGAN.
Show steps
Show steps
Show steps
- Review the provided code and understand the implementation of the generator and discriminator networks.
- Modify the code to experiment with different hyperparameters, such as the number of layers, the number of filters, and the learning rate.
- Create custom data loaders to load your own dataset of images.
- Visualize the generated images to evaluate the performance of the model.
- Compare the performance of different DCGAN architectures and hyperparameter settings.
Build a DCGAN Model to Generate Synthetic Images
Show steps
Demonstrates the application of DCGAN in generating synthetic images.
Show steps
Show steps
Show steps
- Choose a specific domain or application for generating synthetic images, such as generating faces, landscapes, or medical images.
- Collect and preprocess a dataset of real images from the chosen domain.
- Design and implement a DCGAN model tailored to the specific domain.
- Train the DCGAN model on the collected dataset.
- Evaluate the performance of the model and compare it with existing methods.
Contribute to an Open-Source DCGAN Project
Show steps
Provides real-world experience and exposure to best practices.
Show steps
Show steps
Show steps
- Identify an open-source DCGAN project that aligns with your interests and skills.
- Familiarize yourself with the project's codebase and documentation.
- Propose and implement a new feature or improvement to the project.
- Contribute your changes back to the project and engage with the community.
- Review and provide feedback on contributions from other developers.
Career center
Learners who complete Deep Learning with PyTorch : Generative Adversarial Network will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, build, and maintain machine learning systems. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for machine learning engineers who want to work on projects involving image or text generation.
Data Scientist
Data Scientist
Data scientists use their understanding of neural networks and machine learning to help businesses make better decisions based on their data. This course will teach you the basics of DCGANs, which are a type of neural network that can be used to generate new data. This skill can be helpful for data scientists who want to work on projects involving image or text generation.
Computer Vision Engineer
Computer Vision Engineer
Computer vision engineers design and develop systems that can see and understand the world around them. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for computer vision engineers who want to work on projects involving image or text generation.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for software engineers who want to work on projects involving image or text generation.
Data Analyst
Data Analyst
Data analysts use their understanding of data to help businesses make better decisions. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for data analysts who want to work on projects involving image or text generation.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural language processing engineers design and develop systems that can understand and generate human language. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for text generation. This skill can be helpful for natural language processing engineers who want to work on projects involving image or text generation.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial intelligence engineers design and develop systems that can think and learn like humans. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for artificial intelligence engineers who want to work on projects involving image or text generation.
Robotics Engineer
Robotics Engineer
Robotics engineers design, develop, and maintain robots. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for robotics engineers who want to work on projects involving image or text generation.
Game Developer
Game Developer
Game developers design and develop video games. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for game developers who want to work on projects involving image or text generation.
Research Scientist
Research Scientist
Research scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for research scientists who want to work on projects involving image or text generation.
Product Manager
Product Manager
Product managers are responsible for the development and launch of new products. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for product managers who want to work on projects involving image or text generation.
Sales Manager
Sales Manager
Sales managers are responsible for leading and motivating sales teams. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for sales managers who want to work on projects involving image or text generation.
Marketing Manager
Marketing Manager
Marketing managers are responsible for developing and executing marketing campaigns. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for marketing managers who want to work on projects involving image or text generation.
Consultant
Consultant
Consultants provide advice to businesses on a variety of topics, including strategy, operations, and technology. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for consultants who want to work on projects involving image or text generation.
Financial Analyst
Financial Analyst
Financial analysts provide advice to businesses and individuals on financial matters. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for financial analysts who want to work on projects involving image or text generation.
For more career information including salaries, visit:
OpenCourser.com/course/ogb9sb/deep
Reading list
We've selected 11 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
Deep Learning with PyTorch : Generative Adversarial Network.
Save
Provides a comprehensive overview of the theory and practice of generative adversarial networks (GANs). It covers the basic principles of GANs, as well as more advanced topics such as training GANs, evaluating GANs, and using GANs for various applications.
Provides a practical introduction to deep learning with PyTorch. It covers the basics of PyTorch, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers the basics of machine learning, as well as more advanced topics such as deep learning, natural language processing, and computer vision.
Provides a comprehensive overview of deep learning for natural language processing. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and transformers.
Provides a comprehensive overview of reinforcement learning. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
Provides a comprehensive overview of unsupervised learning. It covers the basics of unsupervised learning, as well as more advanced topics such as clustering, dimensionality reduction, and generative models.
Provides a practical introduction to deep learning with Fastai and PyTorch. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical introduction to deep learning from scratch. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical introduction to machine learning for hackers. It covers the basics of machine learning, as well as more advanced topics such as data wrangling, feature engineering, and model evaluation.
Provides a concise overview of machine learning. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning.
Provides a practical introduction to machine learning. It covers the basics of machine learning, as well as more advanced topics such as data wrangling, feature engineering, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ogb9sb/deep
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Focuses on implementing Deep Convolutional Generative Adversarial Networks (DCGANs) in PyTorch to develop handwritten digits
Builds a foundation in implementing and comprehending DCGAN architectures for real-world applications
Utilizes PyTorch, a respected deep learning framework, for implementing and training DCGANs
Provides practical knowledge and hands-on experience in building DCGANs for generating realistic handwritten digits
Suitable for learners familiar with convolutional neural networks and optimization algorithms
Course content focuses primarily on the practical aspects of DCGAN implementation, with less emphasis on theoretical concepts
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