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
Deep Learning with PyTorch : Generative Adversarial Network
Instructor: Parth Dhameliya
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4.5
(77 reviews)
Intermediate level
Recommended experience
2 hours
Learn at your own pace
Hands-on learning
What you'll learn
Create Discriminator and Generator Network
Create a training loop to train GAN model
Skills you'll practice
Details to know
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Taught in English
No downloads or installation required
Only available on desktop
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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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4.5
77 reviews
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HG
5
Reviewed on Sep 23, 2024
best choice for those who know theory and math behind GAN(highly recommended)
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