How-To Geek on MSN

Generate realistic test data in Python fast. No dataset required

Learn the NumPy trick for generating synthetic data that actually behaves like real data.

Understanding Hedonic Regression in Pricing Models

Learn how hedonic regression helps estimate factors affecting prices in real estate and consumer goods, aiding in precise ...
InfoQ

MIT's Recursive Language Models Improve Performance on Long-Context Tasks

Researchers at MIT's CSAIL published a design for Recursive Language Models (RLM), a technique for improving LLM performance on long-context tasks. RLMs use a programming environment to recursively ...
Dot Physics on MSN

How to model a pendulum in Python using Jupyter notebooks

How to model a pendulum in Python using Jupyter Notebooks. This video walks through the physics of pendulum motion and shows how to simulate it step by step with clean Python code and clear ...
IEEE

Big Data-Driven Risk Assessment Based on Regression Model in Logistics

Abstract: We investigated the impact of big data on risk reduction in logistics, leveraging digital technologies and risk management. Utilizing panel data from 118 Chinese A-share listed logistics ...
GitHub

Linear Regression Practice Using Python

From the first 5 rows of the dataset, we can see that there are several columns available: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, and sex. There also appears ...
IEEE

Bearing RUL Prediction Using Composite Health Index-Based Gaussian Process Regression Model With a Dynamic Change Point

Abstract: Predicting the remaining useful life (RUL) of bearings is crucial for system reliability and preventing unexpected shutdowns. Aiming at two critical issues in RUL prediction, the first ...
GitHub

D-FINE: Redefine Regression Task of DETRs as Fine‑grained Distribution Refinement

If you like D-FINE, please give us a ⭐! Your support motivates us to keep improving! D-FINE is a powerful real-time object detector that redefines the bounding box regression task in DETRs as ...