Deep Learning with PyTorch : Generative Adversarial Network
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.
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- Level Intermediate
- Duration 2 hours
- Course by Coursera Project Network
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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.Modules
Deep Learning with PyTorch : Generative Adversarial Network
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Assignment
- Graded Quiz: Test your Project understanding
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- Deep Learning with PyTorch : Generative Adversarial Network
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- Project-based Course Overview
Auto Summary
Dive into the world of Deep Learning with PyTorch through this engaging and practical course on Generative Adversarial Networks (GANs). Designed for IT and Computer Science enthusiasts, this intermediate-level, project-based course spans two hours and focuses on implementing a Deep Convolutional GAN (DCGAN) to generate handwritten digit images. Guided by Coursera, you will learn to create a generator that produces realistic images and a discriminator that distinguishes between real and fake images. The course emphasizes hands-on learning, ensuring you gain practical skills in building and training DCGANs using PyTorch. To excel, you should have a foundational understanding of convolutional neural networks and optimization algorithms such as Adam or gradient descent. While the course is currently optimized for learners in North America, efforts are underway to extend the same experience globally. Join this free course to enhance your deep learning expertise and create impressive image generation models.
Parth Dhameliya