Generative models are gaining a lot of popularity recently among data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labelled data. In GAN, a generator function learns to synthesize samples that best resemble some dataset, while a discriminator function learns to distinguish between samples drawn from the dataset and samples synthesized by the generator. GANs have emerged as a promising framework for unsupervised learning: GAN generators are able to produce images of unprecedented visual quality, while GAN discriminators learn features with rich semantics that lead to state-of-the-art semi-supervised learning.
In Generative Adversarial Networks (GAN) - Essentials training course delegates will learn to have a basic understanding of Generative Adversarial Networks (GAN) and its applications.
By attending Generative Adversarial Networks (GAN) - Essentials workshop, delegates will learn:
- Image Generation with Variational Autoencoder
- Image Transformation with Neural Style Transfer and Deep Dream
- Implementation of DC GAN with Tensorflow 2.x
- Conditional GAN and Cycle GAN
- Machine Learning
- AI Developers
- Artificial Intelligence Engineers
- Data Scientists