Optimization in machine learning refers to the process of adjusting a model’s parameters to minimize (or maximize) a specific objective function, typically the loss function. It is a core component of training algorithms and determines how well a model learns...

Quantum Computing is a next-generation computing paradigm that leverages the principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to process information in fundamentally new ways. Unlike classical computers that use bits (0 or 1), quantum computers use qubits, which...

Text-to-Image refers to a class of generative AI models that can create realistic or artistic images from natural language descriptions. These models interpret a user’s prompt—such as “a futuristic city at sunset”—and generate a corresponding image, often within seconds. As...

IoT (Internet of Things) refers to a network of physical devices embedded with sensors, software, and connectivity that enables them to collect and exchange data. These devices range from smart home gadgets to industrial sensors. When connected, they create a...

Manus is an agentic AI system developed by Chinese startup Monica. Unlike traditional chatbots or LLMs that need detailed user prompts, Manus autonomously handles complex, multi-step tasks by coordinating multiple AI models. Its launch in early 2025 marked a major...

Decision Tree is a machine learning algorithm that models decisions as a series of binary or multi-way splits. Each internal node represents a condition, branches represent outcomes, and leaf nodes indicate final predictions or classifications.   Key Features: Tree StructureNodes...

Model parameters are internal variables that a machine learning model adjusts during training. These values guide how the model transforms input data into predictions. A well-tuned set of parameters helps improve model accuracy, while poor parameter values can lead to...

Hyperparameter Tuning is the process of systematically searching for the best combination of hyperparameters—the external configurations that control how a machine learning model learns. Unlike model parameters (which are learned during training), hyperparameters are set before training begins. As a...

Underfitting occurs when a machine learning model is too simplistic to capture the underlying patterns in the data, resulting in poor performance on both training and test sets. It fails to learn the signal from the data, often due to...

Overfitting is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data. An overfitted model performs well on training data but fails...