Webtagr - Technology News Summarizer

Gaussian Processes: A Gentle Introduction

2025-08-18

This blog post provides an accessible introduction to Gaussian processes (GPs), a powerful tool in machine learning. Starting with the fundamentals of multivariate Gaussian distributions, it explains marginalization and conditioning, leading to the core concept of GPs: predicting data by incorporating prior knowledge. Interactive figures and practical examples illustrate how GPs use kernel functions to define covariance matrices, controlling the shape of the predicted function. Bayesian inference updates the model with training data, allowing for prediction of function values and their confidence intervals.

Why Momentum Really Works: A Deep Dive into Gradient Descent Acceleration

2025-04-28

This article delves into the mechanics of momentum in optimization algorithms. By analyzing convex quadratic functions, it reveals how momentum accelerates gradient descent and explains the underlying mathematical principles. The article also explores the limitations of momentum and its combination with stochastic gradient descent, offering insights into future research directions. Using clear language and concrete examples like polynomial regression and image colorization, the article provides a comprehensive understanding of momentum's principles and applications, suitable for readers interested in optimization algorithms.

A Gentle Introduction to Graph Neural Networks: Unveiling the Power of Graph Data

2024-12-20

This article provides an accessible introduction to Graph Neural Networks (GNNs). It begins by explaining graph data and its real-world applications, such as social networks, molecular structures, and text. The article then delves into the core components of GNNs, including message passing, pooling operations, and various types of graph data. By building a modern GNN model step-by-step, it clarifies the role and motivation behind each component's design. Finally, it offers an interactive GNN playground, allowing readers to experience firsthand the construction and prediction process of a GNN model, deepening their understanding.