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Generative Modeling with VAEs and GANs

This project is part of the liveProject series Semi-Supervised Deep Learning with GANs for Melanoma Detection.
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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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you will learn important deep learning tools and techniques that are highly transferable to a wide range of machine learning roles, especially in the field of computer vision.

  • Implement autoencoders (AEs) to reconstruct images
  • When and how to use transposed convolutions
  • Utilizing variational autoencoders (VAE) to generate new images from vectors sampled from the latent distribution
  • Train an unsupervised deep convolutional GAN (DCGAN)

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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