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.
When you start your liveProject, you get full access to the following books for 90 days.
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:
- Intermediate Python
- Basics of PIL
- Basics of Matplotlib
- Basics of NumPy
- Beginner PyTorch
- 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)
- 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.