You Can't Step in the Same River Twice
In my previous articles, we learned about confounders and colliders in observational data that hinder establishing reliable causal relationships. The solution Pearl provided is to draw causal diagrams and use the backdoor criterion to find the sets of con- 23108Murphy2025-03-22
Stable Diffusion: Mastering the Art of Interior Design
A deep dive into Stable Diffusion and its inpainting variant for interior design- 20722Murphy2025-03-22
Ensuring Correct Use of Transformers in Scikit-learn Pipeline
This article will explain how to use Pipeline and Transformers correctly in Scikit-Learn (sklearn) projects to speed up and reuse our model training process. This piece complements and clarifies the official documentation on Pipeline examples and some com- 23262Murphy2025-03-22
Visualizing trade flow in Python maps – Part I: Bi-directional trade flow maps
The exchange of goods and services in exchange for their corresponding values is an intricate part of our daily life. Similarly, countries...- 26255Murphy2025-03-22
A Simple CI/CD Setup for ML Projects
Apply best practices and learn to use GitHub Actions to build robust code- 23481Murphy2025-03-22
Illuminating the Black Box of Textual GenAI
The Need for Insights- 29613Murphy2025-03-22
Intro to Docker Containers for Data Scientists
A practical tutorial for setting up a local dev environment using Docker Container- 20643Murphy2025-03-22
A Guide to 21 Feature Importance Methods and Packages in Machine Learning (with Code)
From the OmniXAI, Shapash, and Dalex interpretability packages to the Boruta, Relief, and Random Forest feature selection algorithms- 22538Murphy2025-03-22
Courage to Learn ML: A Deeper Dive into F1, Recall, Precision, and ROC Curves
F1 Score: Your Key Metric for Imbalanced Data - But Do You Really Know Why?- 25546Murphy2025-03-22
Business Analytics with LangChain and LLMs
A step-by-step tutorial on querying SQL databases with human language- 24968Murphy2025-03-22
Using Server-less Functions to Govern and Monitor Cloud-Based Training Experiments
A simple routine that can save you loads of money- 29781Murphy2025-03-22
Not A/B Testing Everything is Fine
Leading voices in experimentation suggest that you test everything. Some inconvenient truths about A/B testing suggest it's better not to.- 26248Murphy2025-03-22
Visualizing AI and Tech Hype Using Google Trends
A tutorial on how to create slopegraph visualizations for assessing technological trend shifts, such as virtual reality and generative AI.- 26053Murphy2025-03-22
Artificial Bee Colony – How it differs from PSO
Intuition and code implementation for ABC, and exploring where it outperforms Particle Swarm Optimization- 26421Murphy2025-03-22
Beyond English: Implementing a multilingual RAG solution
An introduction to the do's and don'ts when implementing a non-english Retrieval Augmented Generation (RAG) system- 23538Murphy2025-03-22
Stacked Ensembles for Advanced Predictive Modeling With H2O.ai and Optuna
And how I placed top 10% in Europe's largest machine learning competition with them!- 28059Murphy2025-03-22
Machine Learning is Not All You Need: A Case Study on Signature Detection
Machine learning should not be your go-to solution for every task. Consider the KISS principle like Idid for for signature detection- 22240Murphy2025-03-22
Gaussian Head Avatars: A Summary
There has been a recent explosion of Gaussian Splatting papers and the avatar space is no exception. How do they work and are they going to...- 25023Murphy2025-03-22
Test and cover your code today!
A hands-on guide for adding a motivational GitHub action to your repository- 24487Murphy2025-03-22
Running Airflow DAG Only If Another DAG Is Successful
Using Airflow sensors to control the execution of DAGs on a different schedule- 22241Murphy2025-03-22
The current state of continual learning in AI
Why is ChatGPT only trained up until 2021?Optimizing Pandas Code: The Impact of Operation Sequence
Learn how to rearrange your code to achieve significant speed improvements.