AI & Machine Learning Glossary

Glossary

Foundational AI Concepts

Sep 9, 2025

4 min read

Artificial Intelligence

Artificial Intelligence, a term coined in 1955 by Stanford University computer science professor John McCarthy, was originally defined as "the science and engineering of making intelligent machines"

Dec 2, 2025

4 min read

Artificial General Intelligence

Artificial General Intelligence represents a theoretical form of AI that demonstrates a broad range of problem-solving, creativity, and adaptability comparable to human intelligence.

Dec 2, 2025

4 min read

AI Algorithm

An algorithm represents an unambiguous specification of how to solve a class of problems, capable of performing calculation, data processing, and automated reasoning tasks.

Dec 2, 2025

6 min read

Machine Learning

Machine Learning represents a method that helps machines learn from data and improve at performing tasks without being explicitly programmed for every scenario.

Dec 3, 2025

5 min read

Deep Learning

Deep Learning represents a specialized subset of machine learning that utilizes artificial neural networks with multiple layers (typically three or more) to automatically learn hierarchical representations of data.

Machine Learning Fundamentals

Dec 3, 2025

5 min read

Supervised Learning

Supervised Learning represents a fundamental machine learning paradigm where algorithms learn to predict outputs from inputs by training on labeled data—datasets where the correct answer is explicitly provided for each training example.

Dec 3, 2025

5 min read

Unsupervised Learning

Unsupervised Learning encompasses a class of machine learning methods that automatically discover hidden patterns, structures, and relationships within unlabeled data—datasets where ground truth target variables are not provided.

Dec 4, 2025

5 min read

Semi-Supervised Learning

Semi-supervised Learning combines elements of both supervised and unsupervised learning by utilizing both labeled and unlabeled data during training.

Dec 4, 2025

5 min read

Classification

Classification is the task of predicting which category or class a new instance belongs to, learned from labeled historical examples.

Dec 4, 2025

4 min read

Regression

Semi-supervised Learning combines elements of both supervised and unsupervised learning by utilizing both labeled and unlabeled data during training.

Dec 8, 2025

5 min read

Clustering

Unlike classification which uses predefined categories, clustering discovers natural data groupings by analyzing similarity patterns in unlabeled datasets.

Dec 8, 2025

5 min read

Feature Engineering

Feature engineering bridges raw data and algorithm requirements by engineering input features that encode domain knowledge and improve predictive model performance.

Dec 8, 2025

5 min read

Cross-validation

Cross-validation partitions data into multiple subsets to provide robust, reliable estimates of model performance and generalization to new, unseen data.

Dec 8, 2025

4 min read

Overfitting

Overfitting represents the challenge of balancing model complexity against generalization, where models that perform perfectly on training data fail to predict accurately on new instances.

Dec 8, 2025

4 min read

Generalization

Generalization enables machine learning models to transfer learned knowledge to real-world scenarios by maintaining performance on data significantly different from training examples.

Dec 8, 2025

4 min read

Hyperparameters

Hyperparameters are configuration settings that control the learning process and must be set before training, fundamentally shaping model performance and training efficiency.

Deep Learning and Neural Networks

Dec 8, 2025

4 min read

Neural Networks

Neural networks consist of interconnected artificial neurons organized in layers that automatically discover relevant features and patterns in data without explicit programming.

Dec 8, 2025

3 min read

Activation Function

Activation functions introduce non-linearity into neural networks, enabling them to learn complex relationships that would be impossible with linear transformations alone.

Dec 8, 2025

4 min read

Backpropagation

Backpropagation connects error measurements to parameter adjustments by computing error gradients, enabling networks to learn patterns from data iteratively.

Dec 8, 2025

4 min read

Convolutional Neural Networks

Convolutional neural networks apply learned filters across images to detect spatial hierarchies of features, from simple edges to complex objects, revolutionizing computer vision tasks.

Dec 9, 2025

3 min read

Recurrent Neural Networks

Recurrent neural networks process sequential data by maintaining memory states that evolve across time steps, enabling networks to leverage temporal context and long-range dependencies.

Dec 9, 2025

4 min read

Long Short-Term Memory

Recurrent neural networks process sequential data by maintaining memory states that evolve across time steps, enabling networks to leverage temporal context and long-range dependencies.

Dec 9, 2025

4 min read

Transformer Architecture

Transformers have revolutionized deep learning by enabling parallel processing of sequences through attention mechanisms, becoming the foundation for modern large language models like GPT and BERT.

Dec 10, 2025

5 min read

Attention Mechanism

Attention mechanisms enable neural networks to dynamically assign importance weights to input elements, allowing models to focus on relevant information rather than processing all inputs equally.

Dec 10, 2025

5 min read

Self-Attention

Self-attention enables each position in a sequence to attend to all other positions, capturing relationships and dependencies regardless of distance while allowing full parallel processing.

Dec 10, 2025

5 min read

Multi-Head Attention

Multi-head attention runs multiple independent attention operations in parallel, enabling models to simultaneously capture different types of relationships and patterns from different representation subspaces.

Dec 11, 2025

7 min read

Gradient Descent

Gradient descent iteratively adjusts model parameters by moving in the direction of steepest descent to minimize loss functions, forming the foundational optimization approach for training neural networks.

Dec 11, 2025

9 min read

Loss Function

Loss functions quantify prediction error, providing differentiable objectives that guide neural network optimization through gradient descent toward minimized model performance metrics.

Natural Language Processing

Dec 11, 2025

7 min read

Natural Language Processing

Natural Language Processing combines computational linguistics with machine learning to enable computers to understand, interpret, and generate human language in written and spoken forms.

Dec 12, 2025

9 min read

Large Language Models (LLMs)

Large Language Models employ transformer architectures with billions of parameters trained on massive text corpora, enabling sophisticated language understanding, reasoning, and generation through self-supervised learning at scale.

Dec 12, 2025

8 min read

Tokens

Tokens represent discrete units created through tokenization that break text into manageable pieces for language model processing, balancing vocabulary size, sequence length, and computational efficiency.

Dec 12, 2025

8 min read

Embeddings

Embeddings represent data as dense vectors in continuous spaces where geometric relationships encode semantic similarity, enabling neural networks to perform mathematical operations on symbolic information.

Dec 15, 2025

8 min read

Prompt Engineering

Prompt Engineering designs and optimizes text inputs to guide language models toward desired outputs, enabling task adaptation through careful instruction formulation without requiring model retraining or fine-tuning.

Dec 16, 2025

8 min read

Fine-tuning

Fine-tuning adapts pre-trained models to specific tasks by continuing training on smaller, domain-specific datasets, dramatically reducing computational requirements while preserving pre-trained knowledge and capabilities.

Glossary

Foundational AI Concepts

Artificial Intelligence

Artificial General Intelligence

AI Algorithm

Machine Learning

Deep Learning

Machine Learning Fundamentals

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Classification

Regression

Clustering

Feature Engineering

Cross-validation

Overfitting

Generalization

Hyperparameters

Deep Learning and Neural Networks

Neural Networks

Activation Function

Backpropagation

Convolutional Neural Networks

Recurrent Neural Networks

Long Short-Term Memory

Transformer Architecture

Attention Mechanism

Self-Attention

Multi-Head Attention

Gradient Descent

Loss Function

Natural Language Processing

Natural Language Processing

Large Language Models (LLMs)

Tokens

Embeddings

Prompt Engineering

Fine-tuning

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