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Conor Lazarou
Conor Lazarou

1.1K Followers

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Published in Towards Data Science

·Pinned

I Generated Thousands of New Pokemon using AI

and I am so, so sorry. — Growing up in the nineties and the noughties, I loved Pokemon. Pokemon Red was my first and favourite game, and I easily spent hundreds of hours playing it as a kid despite being too dumb to actually beat it. When I wasn’t trying to catch ’em all, I would make…

Pokemon

7 min read

I Generated Thousands of New Pokemon using AI
I Generated Thousands of New Pokemon using AI
Pokemon

7 min read


Published in Towards Data Science

·Dec 10, 2021

The Bias-Variance Tradeoff

A (Nearly) Stats-Free Adventure — Whether you’re a practicing data scientist or a budding data student, you’re probably familiar with the bias-variance tradeoff. It’s an essential factor in model type selection, right up there with the accuracy-explainability tradeoff. But, how well do you understand it on an intuitive level? …

Machine Learning

9 min read

The Bias-Variance Tradeoff
The Bias-Variance Tradeoff
Machine Learning

9 min read


Published in Towards Data Science

·Sep 19, 2020

Bayesian Thinking

How a Statistician Reacts to Life on Venus — If you’re the sort of person who reads data science blogs (well hello there!) then you probably already know about Bayes’ Law. Heck, you’ve probably even used it yourself. Unfortunately, in my experience, many people only know Bayes’ Law in a technical, academic context, and don’t actually understand it in…

Statistics

7 min read

Bayesian Thinking
Bayesian Thinking
Statistics

7 min read


Published in Towards Data Science

·Jun 26, 2020

How to Build a DCGAN with PyTorch

A Jump-Start GAN Tutorial — In this tutorial, we’ll be building a simple DCGAN in PyTorch and training it to generate handwritten digits. As part of this tutorial we’ll be discussing the PyTorch DataLoader and how to use it to feed real image data into a PyTorch neural network for training. …

Machine Learning

11 min read

How to Build a DCGAN with PyTorch
How to Build a DCGAN with PyTorch
Machine Learning

11 min read


Published in Towards Data Science

·Jun 22, 2020

PyTorch and GANs: A Micro Tutorial

Building the Simplest of GANs in PyTorch — I spent a long time making GANs in TensorFlow/Keras. Too long, honestly, because change is hard. It took some convincing, but I eventually bit the bullet and swapped over to PyTorch. Unfortunately, most of the PyTorch GAN tutorials I’ve come across were overly-complex, focused more on GAN theory than application…

Data Science

11 min read

PyTorch and GANs: A Micro Tutorial
PyTorch and GANs: A Micro Tutorial
Data Science

11 min read


Published in Towards Data Science

·Apr 18, 2020

Autoencoding Generative Adversarial Networks

How the AEGAN architecture stabilizes GAN training and prevents mode collapse — GANs are hard to train. When they work, they work wonders, but anyone who’s tried to train one themselves knows they’re damn finicky bastards. Two of the most common problems in GAN training are mode collapse and lack of convergence. In mode collapse, the generator learns to only generate a…

Machine Learning

9 min read

Autoencoding Generative Adversarial Networks
Autoencoding Generative Adversarial Networks
Machine Learning

9 min read


Published in Towards Data Science

·Mar 22, 2020

from sklearn import *

and other dead-giveaways that you’re a fake data scientist — These days it seems like everyone and their dog are marketing themselves as data scientists—and you can hardly blame them, with “data scientist” being declared the Sexiest Job of the Century and carrying the salary to boot. Still, blame them we will, since many of these posers grift their way…

Data Science

9 min read

from sklearn import *
from sklearn import *
Data Science

9 min read


Published in Towards Data Science

·Feb 26, 2020

Why Do GANs Need So Much Noise?

Visualizing how GANs learn in low-dimensional latent spaces — Generative Adversarial Networks (GANs) are a tool for generating new, “fake” samples given a set of old, “real” samples. These samples can be practically anything: hand-drawn digits, photographs of faces, expressionist paintings, you name it. To do this, GANs learn the underlying distribution behind the original dataset. Throughout training, the…

Machine Learning

8 min read

Why Do GANs Need So Much Noise?
Why Do GANs Need So Much Noise?
Machine Learning

8 min read


Published in Towards Data Science

·Jan 27, 2020

GANs and Inefficient Mappings

How GANs tie themselves in knots and why that impairs both training and quality — A warning to mobile users: this article has some chunky gifs in it. Generative Adversarial Networks (GANs) are being hailed as the Next Big Thing™️ in generative art, and with good reason. New technology has always been a driving factor in art — from the invention…

Machine Learning

9 min read

GANs and Inefficient Mappings
GANs and Inefficient Mappings
Machine Learning

9 min read


Published in Towards Data Science

·Jan 12, 2020

Training a GAN to Sample from the Normal Distribution

Visualizing the Very Basics of Generative Adversarial Networks — In the original Generative Adversarial Network paper, Ian Goodfellow describes a simple GAN which, when trained, is able to generate samples indiscernible from those sampled from the normal distribution. This process is illustrated here: The simplest solution for this task is for the GAN to approximate the inverse CDF of…

Machine Learning

6 min read

Training a GAN to Sample from the Normal Distribution
Training a GAN to Sample from the Normal Distribution
Machine Learning

6 min read

Conor Lazarou

Conor Lazarou

1.1K Followers

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

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