Understanding Generative Adversarial Networks

If you see the above image and it does not make much sense, this article is written for you. I explain how GAN works using a simple project that generates hand-written digit images.

I use Keras on TensorFlow and the notebook code is available in my Github.

Background

Founder & CEO @ kikaben.com | C++, PyTorch | Machine Intelligence Enthusiast | twitter.com/naokishibuya

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

CNN vs. RNN: What’s the Difference?

Overview of the different approaches to putting Machine Learning (ML) models in production

The Most Awesome Loss Function

Computer Vision Foundations Nanodegree — A review.

iOS 13 What’s New

ML, how far!

Word Embedding Lookup

Localization and Object Detection with Deep Learning

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Naoki

Naoki

Founder & CEO @ kikaben.com | C++, PyTorch | Machine Intelligence Enthusiast | twitter.com/naokishibuya

More from Medium

Popular Self-supervised learning methods: SimCLR and SwAV

Generative modeling

Vision Transformers for Femur Fracture Classification

Generative Adversarial Networks 102: DCGAN & Mode Collapse