In this tutorial, we implement a production-grade, large-scale graph analytics pipeline in NetworKit, focusing on speed, memory efficiency, and version-safe APIs in NetworKit 11.2.1. We generate a large-scale free network, extract the largest connected component, and then compute structural backbone signals via k-core decomposition and centrality ranking. We also detect communities with PLM and…

Microsoft has released Phi-4-reasoning-vision-15B, a 15 billion parameter open-weight multimodal reasoning model designed for image and text tasks that require both perception and selective reasoning. It is a compact model built to balance reasoning quality, compute efficiency, and training-data requirements, with particular strength in scientific and mathematical reasoning and understanding user interfaces. https://arxiv.org/pdf/2603.03975 What…

Google has officially released TensorFlow 2.21. The most significant update in this release is the graduation of LiteRT from its preview stage to a fully production-ready stack. Moving forward, LiteRT serves as the universal on-device inference framework, officially replacing TensorFlow Lite (TFLite). This update streamlines the deployment of machine learning models to mobile and…

In this article, you will learn how to fuse dense LLM sentence embeddings, sparse TF-IDF features, and structured metadata into a single scikit-learn pipeline for text classification. Topics we will cover include: Loading and preparing a text dataset alongside synthetic metadata features. Building parallel feature pipelines for TF-IDF, LLM embeddings, and numeric metadata. Fusing…

In this article, you will learn how key-value (KV) caching eliminates redundant computation in autoregressive transformer inference to dramatically improve generation speed. Topics we will cover include: Why autoregressive generation has quadratic computational complexity How the attention mechanism produces query, key, and value representations How KV caching works in practice, including pseudocode and memory…

Can LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach – MachineLearningMastery.com Can LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach – MachineLearningMastery.com Source link