Editors Pick

In today’s data-driven world, valuable insights are often buried in unstructured text—be it clinical notes, lengthy legal contracts, or customer feedback threads. Extracting meaningful, traceable information from these documents is both a technical and practical challenge. Google AI’s new open-source Python library, LangExtract, is designed to address this gap directly, using LLMs like Gemini to deliver…

In this tutorial, we’ll explore a range of SHAP-IQ visualizations that provide insights into how a machine learning model arrives at its predictions. These visuals help break down complex model behavior into interpretable components—revealing both the individual and interactive contributions of features to a specific prediction. Check out the Full Codes here. Installing the dependencies Copy…

In this advanced Roboflow Supervision tutorial, we build a complete object detection pipeline with the Supervision library. We begin by setting up real-time object tracking using ByteTracker, adding detection smoothing, and defining polygon zones to monitor specific regions in a video stream. As we process the frames, we annotate them with bounding boxes, object IDs,…

In this tutorial, we explore how to use the SHAP-IQ package to uncover and visualize feature interactions in machine learning models using Shapley Interaction Indices (SII), building on the foundation of traditional Shapley values. Shapley values are great for explaining individual feature contributions in AI models but fail to capture feature interactions. Shapley interactions go…

Large language models (LLMs) specialized for coding are now integral to software development, driving productivity through code generation, bug fixing, documentation, and refactoring. The fierce competition among commercial and open-source models has led to rapid advancement as well as a proliferation of benchmarks designed to objectively measure coding performance and developer utility. Here’s a detailed,…