Generative Modeling with VAEs and GANs

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prerequisites
intermediate Python • intermediate deep learning • beginner PyTorch • basics of neural networks and image classification
skills learned
implement autoencoders to reconstruct images • utilize variational autoencoders for image generation • train an unsupervised deep convolutional GAN
Olga Petrova
1 week · 8-10 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll utilize unlabeled data and unsupervised machine learning techniques to build and train data generative models. You’ll employ generative modeling, such as a variational autoencoder (VAE) that can generate new images by sampling from the latent distribution. You’ll then use an unsupervised generative adversarial network to generate new images.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Olga Petrova
Olga Petrova is a machine learning engineer at Scaleway, a French cloud provider, where her focus lies on deep learning R&D. Previously, she has worked as a researcher in theoretical physics, looking into the applications of artificial intelligence to quantum systems. Olga has a Ph.D. from Johns Hopkins University, and a B.S. from Worcester Polytechnic Institute. She enjoys blogging about the latest advancements in AI.

prerequisites

This liveProject is for intermediate Python programmers with some deep learning experience. No prior experience in generative modeling, including GANs, is assumed. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basics of PIL
  • Basics of Matplotlib
  • Basics of NumPy
  • Beginner PyTorch
TECHNIQUES
  • Training and evaluating (supervised) deep learning models
  • Basics of neural networks
  • Intermediate deep learning concepts such as convolutional neural networks
  • Basics of linear algebra and statistics

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