Marton Trencseni - Sun 08 May 2022 • Tagged with python, decorators
I show toy implementations of Python decorator patterns such as @measure, @repeat, @trace, @count, @singleton, and @app.route (made famous by Flask).
Marton Trencseni - Thu 05 May 2022 • Tagged with python, enum
I extend my previous toy implementation of Python's Enum class to add more features.
Marton Trencseni - Tue 03 May 2022 • Tagged with python, enum
I write a toy implementation of Python's Enum class to learn about Python metaclasses.
Marton Trencseni - Fri 22 April 2022 • Tagged with python, types
I show slightly more advanced aspects of type checking in Python for Data Scientists.
Marton Trencseni - Sun 17 April 2022 • Tagged with python, types
I show slightly more advanced uses of type checking in Python.
Marton Trencseni - Fri 08 April 2022 • Tagged with python, types
I show how to use basic type hints and get type checking working in ipython notebooks.
Marton Trencseni - Sat 26 March 2022 • Tagged with interview, python
Recently I was considering whether to introduce some CS style algorithmic interview questions into our Data Science hiring loop, since having an understanding of algorithms and data structures can be useful for Data Scientists. Not having done this soft of interview for a few years I picked up my copy of Daily Coding Problem and starting solving a few problems to refresh my feeling for what it feels like as a candidate, and whether it would give us any useful signals.
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.
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.
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.
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.
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.
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.
Marton Trencseni - Sat 01 February 2020 • Tagged with data, airflow, python
Sometimes I get to put on my Data Engineering hat for a few days. I enjoy this because I like to move up and down the Data Science stack and I try to keep myself sharp technically. Recently I was able to spend a few days optimizing our Airflow ETL for speed.
Marton Trencseni - Thu 14 November 2019 • Tagged with python, pytorch, reinforcement, learning, openai, gym
I use simulated self-play by ranking episodes by summed reward. Game outcomes are divided in two by cutting at the median, winners are assigned +1 rewards, losers are assigned -1 rewards, like in games like Go and Chess. Unlike naive policy gradient descent used in previous posts, this version solves all OpenAI classic control problems, albeit slowly.
Marton Trencseni - Tue 12 November 2019 • Tagged with python, pytorch, reinforcement, learning, openai, gym
I try to generalize the policy gradient algorithm as introduced earlier to solve all the OpenAI classic control problems. It works for CartPole and Acrobot, but not for Pendulum and MountainCar environments.
Marton Trencseni - Tue 22 October 2019 • Tagged with python, pytorch, reinforcement, learning, openai, gym, cartpole
The CartPole problem is the Hello World of Reinforcement Learning, originally described in 1985 by Sutton et al. The environment is a pole balanced on a cart. CartPole is one of the environments in OpenAI Gym, so we don't have to code up the physics. Here I walk through a simple solution using Pytorch.
Marton Trencseni - Sun 25 August 2019 • Tagged with python, pytorch, cnn, go
Using historic gameplay between strong Go players as training data, a CNN model is built to predict good Go moves on a standard 19x19 Go board.
Marton Trencseni - Sat 06 July 2019 • Tagged with python, math, pymc3
I use PyMC3 to solve the food delivery toy problem and explore some alternative priors.
Marton Trencseni - Sat 22 June 2019 • Tagged with python, math, fetchr
I was grabbing a burger at Shake Shack, Mall of the Emirates in Dubai, when I noticed this notebook on the counter. The staff is using it to track food deliveries and each service (Carriage, Talabat, UberEats, Deliveroo) has its own column with the order numbers. Let's assume this is the only page for the day, and ask ourselves: given this data, what is the probability that UberEats is the most popular food delivery service?.
