Zero-Shot Learning (ZSL) is a machine learning technique that enables models to recognize and classify objects, concepts, or tasks without having seen any labeled examples of those categories during training. Instead of relying on direct examples, zero-shot models leverage semantic...

YOLO is a real-time object detection algorithm that simultaneously identifies and localizes objects within an image. Instead of performing multiple steps, YOLO uses a single convolutional neural network pass, making it remarkably fast. Although it may sacrifice a bit of...

VLM (Vision Language Model) is a class of AI models that integrate both visual and textual information, enabling the model to understand and generate responses grounded in images and language together. Unlike models that operate solely on text or images,...

A Vector Database is a specialized data storage and retrieval system designed to handle high-dimensional vector embeddings produced by machine learning models. Unlike traditional databases that store structured or textual data, vector databases store numerical representations of complex entities—such as...

Unsupervised Learning is a machine learning paradigm where a model learns patterns directly from unlabeled data. Without predefined labels or targets, the model discovers hidden structures, groupings, or distributions within the dataset. Common unsupervised techniques include clustering, where data is...

Trustworthy AI refers to artificial intelligence systems designed and deployed with an emphasis on reliability, transparency, fairness, and responsibility. These systems not only perform their intended tasks accurately and efficiently but also adhere to ethical guidelines and respect human rights....

Tree of Thoughts is a reasoning framework designed for large language models (LLMs) that guides them to explore multiple reasoning paths before settling on a final answer. Rather than following a single, linear chain of thought, the model branches out...

A Transformer model is a neural network architecture that has revolutionized natural language processing (NLP) and other AI fields by effectively capturing relationships in sequential data without relying solely on recurrent or convolutional operations. Introduced by Vaswani et al. in...

Transfer Learning is a machine learning technique that allows a model trained on one task or domain to be repurposed for another, often related, task or domain. Instead of starting from scratch, the model leverages the knowledge and patterns it...

Training datasets are collections of data used to teach machine learning models the patterns and relationships needed to perform specific tasks. These datasets consist of input features along with corresponding labels or target values, allowing algorithms to learn from examples....