Semi-Supervised Learning with GANs in Keras

Learn how to implement semi-supervised learning using Generative Adversarial Networks (GANs) in Keras through this comprehensive tutorial video. Explore the concept of training models on partially labeled datasets, combining unsupervised and supervised learning approaches. Discover the advantages of using SGANs for achieving better accuracy with limited labeled data compared to traditional CNNs. Follow along as the instructor guides you through the implementation process, covering topics such as standard classification, GAN generator creation, unsupervised discriminator training, and supervised sample integration. Gain practical insights into coding SGANs and understand their potential applications in scenarios with large, partially labeled datasets.

Introduction
Standard classification implementation
GAN generator
Unsupervised discriminator
Supervised samples
Training
Coding