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. It...

The Internet of Things (IoT) refers to a network of physical devices—such as sensors, appliances, vehicles, or machinery—that are embedded with software, connectivity, and intelligence to collect, exchange, and act on data. IoT allows machines to operate autonomously, communicate with...

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 learns from data during training.They directly control the model’s behavior and output. Optimization algorithms adjust these values to reduce loss and boost accuracy.   Key Characteristics   Learned During TrainingThe...

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 and have...

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...