Global Investigative Journalism Network

Data Journalism Basics: A Practical Guide

In this GIJN Academy masterclass video, Fabiola Torres, founder of Salud con Lupa, shares a practical approach to data ...
IEEE

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Abstract: Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area ...
Scientific Research Publishing

Boosting Algorithm: An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ()

Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...
Hacker

Fine-Tuning LLMs: A Comprehensive Tutorial

It costs millions of dollars and months of computing time to train a large language model from the ground up. You most likely never need to do it. Fine-tuning lets you adapt pre-trained language ...
IEEE

A Tutorial on Data-Driven Quality of Experience Modeling With Explainable Artificial Intelligence

Abstract: Data-driven Quality of Experience (QoE) modeling using Machine Learning (ML) is a key enabler for future communication networks as it allows accelerated and unbiased QoE modeling while ...
Visual Studio Magazine

Binary Classification Using LightGBM

Dr. James McCaffrey from Microsoft Research presents a full-code, step-by-step tutorial on using the LightGBM tree-based system to perform binary classification (predicting a discrete variable that ...
Visual Studio Magazine

Multi-Class Classification Using LightGBM

Dr. James McCaffrey of Microsoft Research provides a full-code, step-by-step machine learning tutorial on how to use the LightGBM system to perform multi-class classification using Python and the ...
GitHub

01 Gini for classification problem.r

dat$pp <- 1/(1+exp(-as.vector( XX %*% beta0))) # true Bernoulli probability dat$YL <- rbinom(n=nrow(dat), size=1, prob=dat$pp) # learning data dat$YT <- rbinom(n=nrow ...
GitHub

classification_with_tfdf.py

Description: Using TensorFlow Decision Forests for structured data classification. that are compatible with Keras APIs. The models include [Random Forests](https ...