Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and ...

Abstract: This paper considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents ... Iteratively ...

In this video, we will implement Multiple Linear Regression in Python from Scratch on a Real World House Price dataset. We will not use built-in model, but we will make our own model. This can be ...

pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with ...

As one of the important statistical methods, quantile regression(QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions ...

Predicting car prices using multiple linear regression. This project uses real-world automotive data to train a machine learning model capable of estimating car prices based on technical ...

Abstract: This paper presents a novel methodology to address multi-output regression problems through the incorporation of deep-neural networks and gradient boosting. The proposed approach involves ...

xkcd #2048 is exceptionally relevant to this. Doing linear regression well with a big dataset is difficult! I do this all the time at work and honestly I often show a scatter plot without any ...