Causal AI, exploring the integration of causal reasoning into machine learning This article gives a practical introduction to the potential of causal graphs. It is aimed at anyone who wants to understand more about: What causal graphs are and how they work A worked case study in Python illustrating how to build causal graphs How…

Thermal sharpening of Sentinel-3 images: From 1 Km to 10m using Python in Google Colab 13 min read · 11 hours ago Sentinel-3 thermal image downscaled from 1000 m to 10 m, visualized by the author.🌅 Introduction 💾 Downloading Sentinel-3 (1000 m) and Sentinel-2 images (10 m) ⚙️ Sentinel-3 Image Processing 🌡️…

Estimating your chances of winning the lottery with sampling Statistical estimates can be fascinating, can’t they? By just sampling a few instances from a population, you can infer properties of that population such as the mean value or the variance. Likewise, under the right circumstances, it is possible to estimate the total size of the…

Let’s implement a regression example where the aim is to train a network to predict the value of a node given the value of all other nodes i.e. each node has a single feature (which is a scalar value). The aim of this example is to leverage the inherent relational information encoded in the graph…

Learn how to build neural networks for direct causal inference Photo by Geranimo on UnsplashBuilding machine learning models is fairly easy nowadays, but often, making good predictions is not enough. On top, we want to make causal statements about interventions. Knowing with high accuracy that a customer will leave our company is good, but knowing…

A Brief Tutorial Photo by Nabeel Hussain on UnsplashK-Means is a popular unsupervised algorithm for clustering tasks. Despite its popularity, it can be difficult to use in some contexts due to the requirement that the number of clusters (or k) be chosen before the algorithm has been implemented. Two quantitative methods to address this issue…

How to use UMAP dimensionality reduction for Embeddings to show multiple evaluation Questions and their relationships to source documents with Ragas, OpenAI, Langchain and ChromaDB 13 min read · 19 hours ago Retrieval-Augmented Generation (RAG) adds a retrieval step to the workflow of an LLM, enabling it to query relevant data from…

Quick Success Data Science Learn graphical text analysis with NLTK Sherlock Holmes (by DALL-E3)The Natural Language Tool Kit (NLTK) ships with a fun feature called a dispersion plot that lets you post the location of a word in a text. More specifically, it plots the occurrences of a word versus the number of words from…

To keep things simple and costs to a minimum ETL Pipeline | Image by authorETL stands for Extract, Transform, and Load. An ETL pipeline is essentially just a data transformation process — extracting data from one place, doing something with it, and then loading it back to the same or a different place. If you…

Using scenario based stress testing to identify medium (2050) and long term (2100) sea level rise risks This project utilizes a scenario based qualitative stress testing approach to identify US coastal census tracts expected to adversely impacted by sea level rise (SLR) in the medium (2050) and long term (2100). One Baseline and two ‘plausible…