Marton Trencseni - Sun 24 October 2021 • Tagged with entropy
I discuss 4 uses of entropy in Data Science: (i) cross entropy as a loss function for training neural network classifiers (ii) entropy as a splitting criterion for building decision trees (iii) entropy for evaluating clustering algorithms (iv) entropy for understanding relationships in tabular data. 
Marton Trencseni - Mon 18 October 2021 • Tagged with meta
A review and introspect on the first 100 articles written on Bytepawn. 
Marton Trencseni - Sat 09 October 2021 • Tagged with entropy, cross-entropy, joint-entropy, conditional-entropy, relative-entropy, kullback–leibler-diverence
What's the difference between cross entropy, joint entropy, conditional entropy and relative entropy? 
Marton Trencseni - Sat 25 September 2021 • Tagged with entropy, interviews, cross-entropy, physics
What's the entropy of a fair coin toss? What if the coin almost always returns Heads? My recruiter reports that very few candidates can answer these entropy related DS screening questions. 
Marton Trencseni - Sun 19 September 2021 • Tagged with ab-testing, variance, stratification, cuped
I use toy Monte Carlo simulations to demonstrate 5 ways to reduce variance in A/B testing: increase sample size, move towards a more even split, reduce variance in the metric definition, stratification and CUPED.
Marton Trencseni - Sun 05 September 2021 • Tagged with ab-testing, cuped
In this final blog post about CUPED, I will address some questions about CUPED, such as, is correlation between "before" and "after" the same as seasonality?
Marton Trencseni - Sun 15 August 2021 • Tagged with ab-testing, cuped
I use Monte Carlo simulations of A/A tests to demonstrate how Data Scientists can incorrectly skew lift and p-values if they pick-and-choose between reporting traditional and CUPED results after the experiment has concluded.
Marton Trencseni - Sat 07 August 2021 • Tagged with ab-testing, cuped
I use Monte Carlo simulations of conversion A/B tests to demonstrate how CUPED reduces measurement variance in conversion experiments.
Marton Trencseni - Sat 31 July 2021 • Tagged with ab-testing, cuped
I use Monte Carlo simulations of A/B tests to demonstrate CUPED, a method to use historic "before" data to reduce the variance in the measurement of the treatment lift.
Marton Trencseni - Sun 25 July 2021 • Tagged with redacted
I show how looking at historic "before" values in A/B testing can lead to an apparent paradox.
Marton Trencseni - Fri 23 July 2021 • Tagged with data, fallacies
What are the core skills a data scientist needs to sustainably achieve bottom-line impact, without blocking on external help from other roles?
Marton Trencseni - Tue 20 July 2021 • Tagged with modeling, timeseries, prophet, neuralprophet
I compare Prophet and NeuralProphet performance using a toy forecasting benchmark.
Marton Trencseni - Sun 18 July 2021 • Tagged with modeling, timeseries, prophet
Prophet is a simple to use timeseries forecasting library by Facebook.
Marton Trencseni - Sat 10 July 2021 • Tagged with yolo, yolov5, vision, object detection
I discuss the YOLO neural network architecture for object detection. 
Marton Trencseni - Fri 02 July 2021 • Tagged with yolo, yolov5, vision, object detection
I run object detection experiments with pre-trained YOLOv5 models. 
Marton Trencseni - Sun 20 June 2021 • Tagged with statistics, trump, politics, fasttext, twitter
I train a fasttext classifier on 1.2M data points to predict US politicians' party affiliations from their twitter messages. 
Marton Trencseni - Sat 29 May 2021 • Tagged with statistics
The post explores the distribution of digits of random and non-random numbers from receipts, verifying Benford's law of first digit distribution.
Marton Trencseni - Sat 22 May 2021 • Tagged with hiring, interviewing
Thinking up tricky solutions in 3-5 minutes is not a requirement in a work setting. Usually, there are days or weeks for that. But implementing an idea, once the idea is there, should be straightforward for a good programmer.
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?
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.
