Introduction to Marketing Mix Modeling

Marton Trencseni - Sun 23 July 2023 • Tagged with mmm, marketing, mixed, model, lightweight_mmm, google, python

I describe the concept of Marketing Mix Modeling using Google's LightweightMMM library.

MMM attribution

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GPT News Poet: silly AI poems based on today's news

Marton Trencseni - Sun 07 May 2023 • Tagged with gpt, python, gnews, ai

I show how I used GNews and the OpenAI API to build GPT News Poet, a fun toy site showing silly AI poems based on today's news.

ROC curve

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Can ChatGPT write the Python async message queue codes from the previous posts?

Marton Trencseni - Sun 05 March 2023 • Tagged with python, async, message, queue, chatgpt

I try to get ChatGPT to write the codes in the previous posts. It's able to write the basic message queue skeleton, but cannot implement more complicated features such as delivery semantics with caching.

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Writing a simple Python async message queue server - Part II

Marton Trencseni - Thu 02 March 2023 • Tagged with python, async, message, queue

I write a somewhat more complicated, but still relatively simple async message queue server in Python.

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Writing a simple Python async message queue server - Part I

Marton Trencseni - Mon 27 February 2023 • Tagged with python, async, message, queue

I write a simple, bi-directional async message queue server in Python.

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Finding similar API functions between Pytorch and Tensorflow with Doc2Vec

Marton Trencseni - Wed 21 December 2022 • Tagged with similarity, python, word2vec, doc2vec, pytorch, tensorflow

I use Doc2Vec to try to find pairs of similar API functions between Pytorch and Tensorflow.

Tensorflow Pytorch

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Similar posts recommendation with Doc2Vec - Part III

Marton Trencseni - Sat 10 December 2022 • Tagged with similarity, python, gensim, word2vec, doc2vec, pyml

In the previous posts, I used the Doc2Vec neural network architecture to compute the similarities between my blog posts, and explored the quality of the scores. In this final post, I show how I added the final Articles You May Like recommendation sections to the blog — it's live!

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Similar posts recommendation with Doc2Vec - Part II

Marton Trencseni - Sun 04 December 2022 • Tagged with similarity, python, gensim, word2vec, doc2vec, pyml

In the previous post, I used the Doc2Vec neural network architecture to compute the similarities between my blog posts. In this second post I investigate the results further by examining clusters in graphs.

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Similar posts recommendation with Doc2Vec - Part I

Marton Trencseni - Sat 03 December 2022 • Tagged with similarity, python, gensim, word2vec, doc2vec, pyml

One of the things I learned at Facebook is the power of recommendations. Examples are People You May Know (PYMK), Groups You May Like (GYML) and Pages You May Like (PYML). Inspired by these, I am planning to add an Articles You May Like widget to Bytepawn, based on the semantic similarity of blog posts. I use the Doc2Vec neural network architecture to compute the similarity between my blog posts, and return the top 3 recommendations for each page.

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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?

Marton Trencseni - Wed 30 November 2022 • Tagged with timeseries, prophet, darts, python

One of the most common Data Science tasks in a business setting is timeseries forecasting. I was curious what methods and libraries other Data Scientists use, so I posted an "Ask HN" on Hacker News. The post generated 89 comments, most of them high-quality. This is my summary of the discussion.

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Useful Python decorators for Data Scientists

Marton Trencseni - Sun 22 May 2022 • Tagged with python, decorators

I show toy implementations of Python decorator patterns that may be useful for Data Scientists.

Python decorators

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Building a toy Python @dataclass decorator

Marton Trencseni - Thu 12 May 2022 • Tagged with python, dataclass, decorator

I write a toy implementation of Python's @dataclass decorator to improve my Python fu and learn more about decorators and metaprogramming.

Python enum

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Python decorator patterns

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).

Python enum

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Building a toy Python Enum class - Part II

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.

Python enum

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Building a toy Python Enum class - Part I

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.

Python enum

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Python types for Data Scientists - Part III

Marton Trencseni - Fri 22 April 2022 • Tagged with python, types

I show slightly more advanced aspects of type checking in Python for Data Scientists.

Mypy

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Python types for Data Scientists - Part II

Marton Trencseni - Sun 17 April 2022 • Tagged with python, types

I show slightly more advanced uses of type checking in Python.

Python snake

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Python types for Data Scientists - Part I

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.

Python types for Data Scientists

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Solving 5 algorithmic interview questions

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.

Daily coding problem

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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.

Sample receipt

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