and I am so, so sorry.

I’ll understand if you don’t want to catch ‘em, all. (Image by Author)


How a Statistician Reacts to Life on Venus

Photo by Philippe Donn from Pexels


A Jump-Start GAN Tutorial

Image Source: Pixabay

Requirements

  1. Python 3.7 or higher. Any lower and you’ll have to refactor the f-strings.
  2. PyTorch 1.5 Not sure how to install it? This might help.
  3. Matplotlib 3.1 or higher
  4. Twenty-two minutes or so of your time.


Building the Simplest of GANs in PyTorch

Image Source: Pixabay


How the AEGAN architecture stabilizes GAN training and prevents mode collapse

AEGAN, a two-way street (Image source: Pixabay)


and other dead-giveaways that you’re a fake data scientist

Like most news outlets writing about “hackers”, fake data scientists happily use any random code they find online (image source: Picography)


Visualizing how GANs learn in low-dimensional latent spaces

Image Source: Pexels


How GANs tie themselves in knots and why that impairs both training and quality

Image source: Wikimedia Commons

A warning to mobile users: this article has some chunky gifs in it.


Visualizing the Very Basics of Generative Adversarial Networks

Figure 1: the first figure ever published in a GAN paper, illustrating a GAN learning to map uniform noise to the normal distribution. Black dots are the real data points, the green curve is the distribution generated by the GAN, and the blue curve is the discriminator’s confidence that a sample in that region is real. Here x denotes the sample space and z the latent space. (Source: Goodfellow 2014)


Real-time visualizations of GAN learning and mode collapse

Illustration by Sian Molloy

A warning to mobile users: this article has some chunky gifs in it.

A Forger and a Detective Walk into a Bar

Conor Lazarou

Data science and ML consultant, generative artist, writer. flatland.ai

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