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Learn how to build and train different generative adversarial network architectures to generate new images.
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Become familiar with generative adversarial networks (GANs). Students in this course will learn how to build and train different GANs architectures to generate new images. Discover, build, and train architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on diverse datasets including the MNIST dataset, Summer2Winter Yosemite dataset, or CelebA dataset.
Fully Convolutional Neural Networks, Python Proficiency, PyTorch
Build generator and discriminator using fully connected layers. Implement loss functions and train a custom GAN on the MNIST dataset.
Build generator and discriminator using convolutional, batch normalization, and fully connected layers. Train a DCGAN model on the CIFAR10 dataset and implement evaluation metrics and evaluate generated samples.
Implement unpaired data loader. Build the CycleGAN generator using residual connection and an encoder-decoder structure. Train a CycleGAN model on the summer2winter Yosemite dataset.
Implement Wasserstein loss and gradient penalties and build the ProGAN generator. Implement StyleGAN components (adaptive instance normalization).
Build and train a custom GAN architecture on the CelebA dataset, leveraging the different skills learned during the course. Build a custom GAN architecture, including generator and discriminator. Experiment with the different loss functions discovered during the course, such as the Binary Cross Entropy loss or the Wasserstein loss. Finally, you’ll utilize some of the methods learned to stabilize training, such as label smoothing
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Thomas is originally a geophysicist but his passion for Computer Vision led him to become a Deep Learning engineer at various startups. By creating online courses, he is hoping to make education more accessible. When he is not coding, Thomas can be found in the mountains skiing or climbing.
Learn how to build and train different generative adversarial network architectures to generate new images.
On average, successful students take 1 month to complete this program.
No. This Course accepts all applicants regardless of experience and specific background.
A well-prepared learner has experience with Convolutional neural networks, Recurrent neural networks, Intermediate Python, PyTorch, Basic calculus, Linear algebra, Basic probability, and Jupyter notebooks.
This course is comprised of content and curriculum to support one project. We estimate that students can complete the program in one month.
The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
There are no software and version requirements to complete this course. All coursework and projects can be completed via Student Workspaces in the Udacity online classroom. Udacity’s full technical requirements are listed here.