10 ways to iterate from 0 to 1 with deciles
Marton Trencseni - Fri 14 May 2021 • Tagged with mlflow, tracking
What's the best way to iteratore from 0 to 1 in steps of 0.1 in Python, and what are the potential pitfalls?
Sometimes brute forcing just works
Marton Trencseni - Thu 06 May 2021 • Tagged with python
I describe a real world use-case where a simple, brute force search based solution worked really well, making more sophisticated Machine Learning unnecessary.
Building intuition for p-values and statistical significance
Marton Trencseni - Sun 25 April 2021 • Tagged with ab-testing
This is the transcript of a talk I did on experimentation and A/B testing to give the audience an intuitive understanding of p-values and statistical significance.
Random numbers, the natural logarithm and higher dimensional simplexes
Marton Trencseni - Sat 17 April 2021 • Tagged with bayesian, ab-test
The base $e$ of the natural logarithm shows up in an unexpected place. Let's derive why!
Classification accuracy of quantized Autoencoders with Pytorch and MNIST
Marton Trencseni - Fri 09 April 2021 • Tagged with python, pytorch, cnn, torchvision, mnist, autoencoder
I measure how the classification accuracy of quantized Autoencoder neural network varies with encoding bits on MNIST digits.
Investigating information storage in quantized Autoencoders with Pytorch and MNIST
Marton Trencseni - Sun 04 April 2021 • Tagged with python, pytorch, cnn, torchvision, mnist, autoencoder
I investigate how much information an Autoencoder neural network encodes for MNIST digits.
Building a Pytorch Autoencoder for MNIST digits
Marton Trencseni - Thu 18 March 2021 • Tagged with pytorch, autoencoder, mnist
I build an Autoencoder network to categorize MNIST digits in Pytorch.
The best parts of Invent and Wander: the Collected Writings of Jeff Bezos
Marton Trencseni - Sat 06 March 2021 • Tagged with business, experimentation, book, amazon, management
These are the best parts from the book "Invent and Wander: the Collected Writings of Jeff Bezos". The book is a collection of the annual Amazon shareholder letters that Jeff Bezos has been sending out since 1997, and speeches he has given over time.
Training a Pytorch Wasserstein MNIST GAN on Google Colab
Marton Trencseni - Wed 03 March 2021 • Tagged with python, pytorch, torchvision, mnist, gan
I train a Pytorch Wasserstein MNIST GAN on Google Colab to beautiful MNIST digits.
Training a Pytorch Classic MNIST GAN on Google Colab
Marton Trencseni - Tue 02 March 2021 • Tagged with python, pytorch, torchvision, mnist, gan
I train a Pytorch Classic MNIST GAN on Google Colab to generate MNIST digits.
How I manage notifications to keep myself uninterrupted
Marton Trencseni - Mon 22 February 2021 • Tagged with personal, interruptions, notifications
I love using apps, email, social networking and messaging. But I want to do use them on my own time. So many years ago I decided to not let my phone interrupt me.
Training a Pytorch Lightning MNIST GAN on Google Colab
Marton Trencseni - Sat 20 February 2021 • Tagged with python, pytorch, gan, mnist, google-colab
I explore MNIST digits generated by a Generative Adversarial Network trained on Google Colab using Pytorch Lightning.
Automatic MLFlow logging for Pytorch
Marton Trencseni - Sun 24 January 2021 • Tagged with mlflow, tracking
I explore the automatic logging capabilities of MLFlow for Pytorch.
Automatic MLFlow logging for Scikit Learn
Marton Trencseni - Fri 15 January 2021 • Tagged with mlflow, tracking
I explore the automatic logging capabilities of MLFlow for Scikit Learn. In the process I found a bug in MLFlow, reported it and wrote a pull request to fix it.
Getting Started with MLFlow
Marton Trencseni - Sun 10 January 2021 • Tagged with mlflow, tracking
For the last few months I’ve been using MFlow in production, specifically its Tracking component. MLFlow is an open source project for lifecycle tracking and serving of ML models, coming out of Databricks. MLFlow is model agnostic, so you can use with SKLearn, XGBoost, Pytorch, Tensorflow, FBProphet, anything.
Making statistics lie for the 2020 Presidential election
Marton Trencseni - Thu 17 December 2020 • Tagged with ab-testing, trump, politics
After the 2020 US presidential election, the Trump campaign filed over 50 lawsuits and attacked the integrity of the elections by claiming there was voter fraud. One of the last lawsuits was filed in the Supreme Court of the United States by the state of Texas. Here I look at the statistical claims made in this lawsuit that were supposed to show irregularities in the Georgia vote.
Comparing conversion at control and treatment sites
Marton Trencseni - Thu 03 December 2020 • Tagged with ab-testing
In real-life, non-digital situations, it's often not feasible to run true A/B tests. In such cases, we can compare before and after rollout conversions at a treatment site, while using a similar control site to measure and correct for seasonality. The post discusses how to compute increasingly correct p-values and bayesian probabilities in such scenarios.
Unevenness at the edges
Marton Trencseni - Fri 30 October 2020 • Tagged with stats, data
Sometimes we look at the top performers in a field and see obviously uneven representations of groups (gender, ethnicity, etc). There a multitude of factors that can lead to it, such as unfair bias in access to opportunities. Here I will show one unintuitive mathematical effect that can contribute to such unevenness in the case of normal distributions.
Effective Data Visualization Part 3: Line charts and stacked area charts
Marton Trencseni - Tue 01 September 2020 • Tagged with charts, dashboards, data, visualization
Most charts should be line charts or stacked area chart, because they communicate valuable trend information and are easy to parse for the human eyes and brain.
Effective Data Visualization Part 2: Formatting numbers
Marton Trencseni - Sun 23 August 2020 • Tagged with charts, dashboards, data, visualization
Format numbers for human consumption. What is more readable, 1.539e+5 or 153,859? Showing numbers effectively on spreadsheets, charts, dashboards, reports is a basic ingredient for readability, like formatting code in programming.
