Fabric Madness part 2 Image by author and ChatGPT. “Design an illustration, focusing on a basketball player in action, this time the theme is on using pyspark to generate features for machine leaning models in a graphic novel style” prompt. ChatGPT, 4, OpenAI, 4 April. 2024. https://chat.openai.com.A Huge thanks to Martim Chaves who co-authored this…

A comparative overview What is cuDF Pandas? If you’re a user of the Pandas library in Python, and you want or need to maximise your program run times, then you have a few options available to you. Most of these options revolve around the use of external libraries that supplant existing Pandas operations and are…

Missing puzzle piece to LLM Enterprise Augmentation Since early last year, when we led the development of an enterprise-level GenAI-as-a-service platform, we have understandably been bombarded with questions like “What are the art of possibles for …” or “Can LLM do …” In this blog post, we will dive into a critical skill that will…

Advanced strategies for better customer insights The RFM (Recency, Frequency, Monetary) model, with its simplicity and ease of implementation, remains a great tool for customer relationship management, offering valuable insights into customer behaviour. Building on the groundwork from my previous article “How to Create an RFM Model in BigQuery”, in this article, we will explore…

Let’s begin with the idea of an ‘expert’ in this context. Experts are feed-forward neural networks. We then connect them to our main model via gates that will route the signal to specific experts. You can imagine our neural network thinks of these experts as simply more complex neurons within a layer. Figure 1 from…

tl;dr version: A team of students helped design and carry out an experiment to determine whether bowls of Lucky Charms are equally “lucky” over the course of a box of cereal. Turns out, not so much. We estimate a decrease of approximately 2.7 total charms per additional bowl on average. This corresponds to more than…

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…