AI &amp; ML glossary .

A mathematical function that determines the output of a neuron based on its weighted input sum.

AI Bias

AI Ethics

Systematic errors in AI systems that create unfair outcomes for certain groups.

Algorithm

A set of step-by-step instructions or rules followed to solve a problem or perform a computation.

Artificial Intelligence (AI)

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

Attention Mechanism

A technique that allows models to focus on relevant parts of the input when producing output.

Backpropagation

The algorithm for calculating gradients of the loss function with respect to network weights.

Batch Size

The number of training examples used in one iteration of model training.

BERT

Bidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.

Calculus

Mathematics

The mathematical study of continuous change, including derivatives and integrals.

Classification

Predicting which category or class an input belongs to from a set of predefined categories.

Computer Vision

The field of AI enabling computers to interpret and understand visual information from the world.

Convolutional Neural Network (CNN)

A neural network architecture designed for processing grid-like data such as images.

Cross-Validation

A technique to evaluate model performance by training and testing on different subsets of data.

Data Augmentation

Data Science

Artificially increasing training data by creating modified versions of existing data.

Data Preprocessing

Data Science

Cleaning and transforming raw data into a format suitable for machine learning.

Deep Learning

A subset of machine learning using artificial neural networks with multiple layers to model complex patterns.

Diffusion Model

Generative models that learn to create data by reversing a gradual noising process.

Dropout

A regularization technique that randomly drops neurons during training to prevent overfitting.

Ensemble Learning

Combining multiple models to produce better predictions than any single model.

Epoch

One complete pass through the entire training dataset during model training.

Explainability

AI Ethics

The ability to understand and interpret how an AI model makes its decisions.

Feature

An individual measurable property or characteristic of data used as input to a machine learning model.

Feature Engineering

The process of using domain knowledge to create new features that improve model performance.

Fine-Tuning

Adapting a pre-trained model to a new task by training on task-specific data.

Generative Adversarial Network (GAN)

Two neural networks competing against each other to generate realistic synthetic data.

GPT

Generative Pre-trained Transformer, a family of large language models trained to generate text.

Gradient Descent

An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.

Hallucination

When AI models generate plausible-sounding but factually incorrect or fabricated information.

Hugging Face

A platform and library for sharing and using pre-trained ML models, especially for NLP.

Hyperparameter

Configuration settings set before training that control the learning process, not learned from data.

Hyperparameter Tuning

The process of finding the optimal hyperparameter values for a machine learning model.

Image Segmentation

Computer Vision

Partitioning an image into multiple segments or regions, classifying each pixel.

Inference

The process of using a trained model to make predictions on new, unseen data.

Large Language Model (LLM)

AI models trained on vast text data that can generate and understand human-like text.

Linear Algebra

Mathematics

The branch of mathematics dealing with vectors, matrices, and linear transformations.

Loss Function

A function that measures how far model predictions are from actual values.

LSTM (Long Short-Term Memory)

An RNN variant with gates that control information flow, enabling learning of long-term dependencies.

Machine Learning

A subset of AI where systems automatically learn and improve from experience without being explicitly programmed.

MLflow

An open-source platform for managing the ML lifecycle including experimentation, reproducibility, and deployment.

Model

A mathematical representation learned from data that can make predictions or decisions on new, unseen data.

Named Entity Recognition (NER)

Identifying and classifying named entities in text into categories like person, organization, location.

Natural Language Processing (NLP)

The field of AI focused on enabling computers to understand, interpret, and generate human language.

Neural Network

A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons).

Neuron

A basic computational unit in a neural network that receives inputs, applies weights and activation, and produces output.

Object Detection

Computer Vision

Identifying and localizing objects within an image by drawing bounding boxes around them.

Overfitting

When a model learns training data too well, including noise, and performs poorly on new data.

Probability

Mathematics

The mathematical study of uncertainty and random events.

Prompt Engineering

The practice of crafting effective inputs to get desired outputs from AI models.

PyTorch

An open-source machine learning framework developed by Meta AI, popular in research.

Q-Learning

Reinforcement Learning

A reinforcement learning algorithm that learns the value of actions in states to determine optimal behavior.

RAG (Retrieval-Augmented Generation)

Combining retrieval systems with language models to generate responses grounded in external knowledge.

Recurrent Neural Network (RNN)

A neural network architecture designed for sequential data with connections between nodes forming cycles.

Regression

Predicting a continuous numerical value based on input features.

Reinforcement Learning

Machine learning where an agent learns to make decisions by taking actions and receiving rewards or penalties.

Responsible AI

AI Ethics

Developing and deploying AI systems that are ethical, fair, transparent, and accountable.

Semi-Supervised Learning

Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.

Sentiment Analysis

Determining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.

Supervised Learning

Machine learning approach where models learn from labeled training data to predict outcomes.

TensorFlow

An open-source machine learning framework developed by Google for building and deploying ML models.

Tokenization

Breaking text into smaller units (tokens) such as words, subwords, or characters.

Train-Test Split

Data Science

Dividing data into separate sets for training and evaluating model performance.

Training

The process of teaching a machine learning model to make predictions by exposing it to data and adjusting its parameters.

Transfer Learning

Using knowledge learned from one task to improve performance on a different but related task.

Transformer

A neural network architecture based on self-attention mechanisms, powering modern language models.

Underfitting

When a model is too simple to capture the underlying patterns in the data.

Unsupervised Learning

Machine learning approach where models find patterns in data without labeled examples.

Word Embedding