Generative Adversarial Networks (GANs)

#001 GANs – Introduction To Generative Models

Learn how and why GANs became so popular, their brief history, and their evolution over the past three decades

GANs #002 Supervised vs. Unsupervised Learning and Discriminative vs. Generative

Learn about differences between GANs and classical Deep Learning methods

#003 GANs – Autoencoder implemented with PyTorch

Gain a deeper insight into the working mechanisms of autoencoders, and learn how they can be implemented in PyTorch

GANs #004 Variational Autoencoders – in-depth explained

A deep insight into theory and math behind Variational Autoencoders

#005 GANs – Face editing with Generative Adversarial Networks

Check this post if you want to see what you would look like 50 years from now, or as a part of the opposite gender

#006 GANs – How to Develop a 1D GAN from Scratch

Learn how to build your first 1D GAN from scratch using sine wave function.

#007 How to implement GAN Hacks to Train Stable Models?

The important hacks that will help you to overcome most common chalenges that can occur during the GAN training

#008 The Mathematics of GANs

Learn about mathematical foundations of GANs

#009 Developing a DCGAN for MNIST Dataset

Learn how to train GANs on handwritten digits

#010 Developing a DCGAN for CIFAR-10 Dataset

Build your first DCGAN model using the CIFAR-10 dataset

#011 Developing a DCGAN for CelebA Dataset

Build a DCGAN model using the large-scale CIFAR-10 dataset

#012 Understanding Latent Space in Generators

Understand the Latent Space in a typical GAN model

#013 Conditional Generative Adversarial Networks (CGANs)

Learn how to train a GAN model using a labeled dataset

#014 Pix2Pix Generative Adversarial Networks

Learn about Pix2Pix GAN - one of the most famous GANs being used today