Select Page
Generative Adversarial Network (GAN) Series

Generative Adversarial Network (GAN) Series

The Deep Learning universe grows every day. The whole field is somewhat in the phase of “crossing the chasm” and entering “pragmatists” phase. Simply put, people use deep learning models for practical problems and with that, they come up with different architectures...
Implementing CycleGAN Using Python

Implementing CycleGAN Using Python

There are many variations of Generative Adversarial Networks. GAN Zoo actually became so big that just scrolling through all papers that are utilizing this concept can cause pain in your finger. All jokes aside GANs main concepts changed the world of deep learning....
Introduction to CycleGAN

Introduction to CycleGAN

Generative Adversarial Networks (GAN) has changed the way we observe deep learning field. Up until that point, generative algorithms were a one-side ally, and the engineers were focused more on regression and classification tasks. Different approaches and applications...
Introduction to Adversarial Autoencoders

Introduction to Adversarial Autoencoders

Generative Adversarial Networks (GAN) shook up the deep learning world. When they first appeared in 2014, they proposed a new and fresh approach to modeling and gave a possibility for new neural network architectures to emerge. Since standard GAN architecture is...
Implementing GAN & DCGAN with Python

Implementing GAN & DCGAN with Python

The code that accompanies this article can be downloaded here. In the previous article, we started exploring the vast universe of generative algorithms. We started with a gentle introduction to Generative Adversarial Networks or GANs. This major idea, first...
Subscribe Subscribe to our newsletter and receive our Python Basics Cheatsheet!