This deep learning course provides a comprehensive introduction to Autoencoders, Variational Autoencoders (VAE), and Generative Adversarial Networks (GANs). Begin by exploring how autoencoders compress and reconstruct data, and discover how VAEs add probabilistic modeling to enhance generative capabilities. Learn the VAE training process and implement a VAE using TensorFlow for image generation with the MNIST dataset. Progress to mastering GANs—understand their adversarial training approach, how the generator and discriminator interact, and explore real-world applications. Gain hands-on experience by building a GAN to generate realistic fake images through step-by-step demos.

To be successful in this course, you should have a basic understanding of neural networks, machine learning concepts, and Python programming. By the end of this course, you’ll be able to: - Implement and train autoencoders and VAEs - Apply VAEs for generative tasks like image synthesis - Build and train GANs to generate realistic data - Understand and apply adversarial training in real-world use cases Ideal for aspiring AI developers, ML engineers, and data scientists exploring generative deep learning.