In today’s rapidly evolving AI landscape, privacy, control, and performance are more important than ever. Ollama emerges as a powerful solution, enabling developers and AI enthusiasts to run open-source large language models (LLMs) locally on their own systems. Whether your goal is building AI-powered applications or exploring AI capabilities, Ollama provides a versatile platform tailored to diverse needs. What is Ollama? Ollama is an open-source platform designed to facilitate the local execution of LLMs . By running models directly on your hardware, Ollama ensures full data control , enhanced privacy , and reduced reliance on cloud services . Key Features 1. Local Model Execution Ollama supports running a variety of LLMs—including LLaMA 2, Mistral, and Phi-2 —directly on your machine. This eliminates the need for internet connectivity, keeping your data private and secure . 2. Cross-Platform Compatibility The platform works across macOS, Windows, and Linux , providing f...
Confusion Matrix A confusion matrix is a performance measurement tool for classification problems. It is used to evaluate the accuracy of a classification, particularly in terms of how well it predicts different classes. The matrix compares the predicted classifications to the actual (true) classifications. A typical confusion matrix for a binary classification problem is a 2x2 table, with the following structure: To understand and utilize confusion matrix we need to understand the following terms. As some of the matrices that are calculated will be using those terms. Key Terms: True Positive (TP) : The number of positive instances that were correctly classified as positive. True Negative (TN): The number of negative instances that were correctly classified as negative. False Positive (FP): The number of negative instances that were incorrectly classified as positive (also called a Type I error). False Negative (FN) : The number of positive instances that wer...