Jan 11, 2018 · Artificial Intelligence

Topic Modeling Explained: pLSA, LDA, and How to Pick the Right Number of Topics

This article introduces the fundamentals of topic modeling, compares the probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) methods, explains their graphical models and inference via EM or Gibbs sampling, and discusses practical strategies for selecting the optimal number of topics using perplexity or hierarchical Dirichlet processes.

LDAPerplexitypLSA

0 likes · 10 min read

Topic Modeling Explained: pLSA, LDA, and How to Pick the Right Number of Topics

21CTO

Sep 19, 2015 · Artificial Intelligence

Why Distributed Machine Learning Needs More Data Than Speed

The article explains how distributed machine learning evolved from parallel computing to handle massive, long‑tail data sets, discusses the importance of scalability, fault recovery, and data‑parallel algorithms, and reviews frameworks such as MPI, MapReduce, and Pregel for building large‑scale AI systems.

Big DataData ParallelismLDA

0 likes · 24 min read

Why Distributed Machine Learning Needs More Data Than Speed