Generative Adversarial Networks (GANs)

#001 GANs – Introduction To Generative Models

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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

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Learn about differences between GANs and classical Deep Learning methods

#003 GANs – Autoencoder implemented with PyTorch

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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

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A deep insight into theory and math behind Variational Autoencoders

#005 GANs – Face editing with Generative Adversarial Networks

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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

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

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

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

#008 The Mathematics of GANs

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Learn about mathematical foundations of GANs

#009 Developing a DCGAN for MNIST Dataset

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Learn how to train GANs on handwritten digits

#010 Developing a DCGAN for CIFAR-10 Dataset

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Build your first DCGAN model using the CIFAR-10 dataset

#011 Developing a DCGAN for CelebA Dataset

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

#012 Understanding Latent Space in Generators

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Understand the Latent Space in a typical GAN model

#013 Conditional Generative Adversarial Networks (CGANs)

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Learn how to train a GAN model using a labeled dataset

#014 Pix2Pix Generative Adversarial Networks

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

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