Dr.Tiya Vaj 🇹🇭·Apr 8การสร้าง Synthetic Data (ข้อมูลสังเคราะห์) โดยใช้ Dirichlet Distributionการสร้าง Synthetic Data (ข้อมูลสังเคราะห์) โดยใช้ Dirichlet Distribution เป็นเทคนิคที่นิยมมากในวงการ Data Science…
Gaurab Baral·Feb 3Making LLMs Faster: Using LDA to Organize Data for RAGBig text datasets can be hard for language models to handle. For example, if a dataset has 300 million words , like thousands of books …
Ujang Riswanto·Dec 2, 2025Why Sparse LDA is Ideal for High-Dimensional Text DataDiscover why Sparse LDA is the perfect solution for high-dimensional text data. Learn how it improves efficiency, scalability, and topic…
Ujang Riswanto·Dec 1, 2025Step-by-Step Implementation of Sparse LDA in Python or RSparse LDA is like turning down the background noise in your topic model so the main voices come through loud and clear
Ujang Riswanto·Nov 28, 2025What is Sparse LDA? A Beginner’s Guide to Efficient Topic ModelingLearn what Sparse LDA is and why it’s a game changer for large-scale text analysis. This beginner’s guide explains how Sparse LDA works…
Ujang Riswanto·Nov 27, 2025Optimizing sLDA Parameters for Better Performance in Text PredictionLearn how to optimize Supervised LDA (sLDA) parameters — like topic number, priors, and variance — to boost text prediction accuracy. This…
Ujang Riswanto·Nov 26, 2025Step-by-Step Implementation of sLDA in PythonIf you’ve played around with Latent Dirichlet Allocation (LDA) before, you probably know it’s great at finding hidden themes in text — like…
Ujang Riswanto·Nov 25, 2025What is Supervised LDA? A Beginner’s Guide to Predictive Topic ModelingWhenever you’ve got text + a label you care about, sLDA can step in to both explain what people are talking about and predict outcomes…
Ujang Riswanto·Nov 24, 2025Comparing PAM with LDA, CTM, and hLDA: Which Model is Right for Your Data?Learn the differences between PAM, LDA, CTM, and hLDA in topic modeling. Compare features, strengths, and best use cases to pick the right…
Ujang Riswanto·Nov 19, 2025Challenges and Solutions in Implementing Parallel LDA for Large DatasetsParallelization is what turns LDA from a cool-but-slow academic algorithm into a practical tool for real-world, big-data problems.
