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.



Deep Learning with PyTorch : Generative Adversarial Network

Instructor: Parth Dhameliya
Access provided by Kaveri College of Arts, Science and Commerce
11,042 already enrolled
(98 reviews)
Recommended experience
What you'll learn
- Create Discriminator and Generator Network 
- Create a training loop to train GAN model 
Skills you'll practice
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
- Setup Google Runtime (2 min) 
- Configurations (4 min) 
- Load MNIST Handwritten Dataset (6 min) 
- Load Dataset into Batches (5 min) 
- Create Discriminator Network (12 min) 
- Create Generator Network (15 min) 
- Create Loss Function and Load Optimizers (4 min) 
- Training GAN (14 min) 
Recommended experience
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
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How you'll learn
- Skill-based, hands-on learning - Practice new skills by completing job-related tasks. 
- Expert guidance - Follow along with pre-recorded videos from experts using a unique side-by-side interface. 
- No downloads or installation required - Access the tools and resources you need in a pre-configured cloud workspace. 
- Available only on desktop - This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices. 
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98 reviews
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Reviewed on May 28, 2025
Felt like the instructor was rushing in the last 3 videos, and he did not explain it completely in detail. Rest was very good





