AI Terms Glossary
Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems.
Machine Learning (ML)
A subset of AI that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience.
Deep Learning
A subset of ML that uses neural networks with many layers to analyze various factors of data.
Neural Network
A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Natural Language Processing (NLP)
A field of AI that gives machines the ability to read, understand, and derive meaning from human languages.
Computer Vision
A field of AI that trains computers to interpret and understand the visual world.
Reinforcement Learning
An area of ML where an agent learns to make decisions by taking actions in an environment to achieve maximum cumulative reward.
Vibe Coding
A modern approach to programming that emphasizes creativity and intuition in the coding process.
MCP (Model-Conditioned Programming)
A programming paradigm where models are used to condition the behavior of software systems.
Large Language Model (LLM)
A type of AI model that is trained on vast amounts of text data to understand and generate human-like text.
Gemini
An advanced LLM developed to provide high-quality language understanding and generation capabilities.
Claude
A state-of-the-art LLM known for its conversational abilities and contextual understanding.
GPT (Generative Pre-trained Transformer)
A type of LLM developed by OpenAI, known for its ability to generate human-like text.
BERT (Bidirectional Encoder Representations from Transformers)
A transformer-based model designed to understand the context of words in a sentence.
Transformer
A neural network architecture that uses self-attention mechanisms to process sequential data.
Self-Attention
A mechanism in neural networks that allows the model to weigh the importance of different words in a sentence.
Tokenization
The process of converting text into smaller units, such as words or subwords, for processing by a model.
Zero-Shot Learning
A method where a model can make predictions on tasks it was not explicitly trained on.
Few-Shot Learning
A method where a model learns to perform a task with a very small amount of training data.
Transfer Learning
A technique where a pre-trained model is adapted to a new task with minimal additional training.
Fine-Tuning
The process of adjusting a pre-trained model on a specific dataset to improve its performance on a particular task.
Prompt Engineering
The practice of designing input prompts to elicit desired responses from language models.
Bias in AI
The presence of systematic errors in AI systems that lead to unfair outcomes.
Explainable AI (XAI)
AI systems designed to provide human-understandable explanations for their decisions.
Ethical AI
The practice of developing AI systems that adhere to ethical guidelines and principles.
AI Ethics
The study of moral issues and decisions surrounding AI technologies.
Data Augmentation
A technique used to increase the diversity of training data by applying transformations.
Overfitting
A modeling error that occurs when a model learns the training data too well, including its noise and outliers.
Underfitting
A modeling error that occurs when a model is too simple to capture the underlying trend of the data.
Hyperparameter Tuning
The process of optimizing the parameters that govern the training process of a model.
Gradient Descent
An optimization algorithm used to minimize the loss function in machine learning models.
Backpropagation
A method used in neural networks to calculate the gradient of the loss function with respect to the weights.
Convolutional Neural Network (CNN)
A class of deep neural networks commonly used for analyzing visual imagery.
Recurrent Neural Network (RNN)
A class of neural networks designed to recognize patterns in sequences of data.
Generative Adversarial Network (GAN)
A class of machine learning frameworks designed to generate new data with the same statistics as the training set.
Autoencoder
A type of neural network used to learn efficient codings of input data.
Dimensionality Reduction
The process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Clustering
A type of unsupervised learning that involves grouping data points into clusters based on similarity.
Classification
A type of supervised learning where the model learns to predict the category of input data.
Regression
A type of supervised learning where the model learns to predict a continuous output variable.
Anomaly Detection
The identification of rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
Feature Engineering
The process of using domain knowledge to extract features from raw data to improve the performance of machine learning models.
Data Preprocessing
The process of transforming raw data into a format that is suitable for modeling.
Data Normalization
The process of scaling individual samples to have unit norm.
Data Standardization
The process of rescaling data to have a mean of zero and a standard deviation of one.
Batch Normalization
A technique to improve the training of deep neural networks by normalizing the inputs to each layer.
Dropout
A regularization technique used to prevent overfitting in neural networks by randomly dropping units during training.
Activation Function
A function used in neural networks to introduce non-linearity into the model.
Softmax
An activation function that converts a vector of numbers into a probability distribution.
Sigmoid
An activation function that maps any real-valued number into the range between 0 and 1.
ReLU (Rectified Linear Unit)
An activation function that outputs the input directly if it is positive; otherwise, it will output zero.
Loss Function
A function that measures how well a model's predictions match the actual data.
Cross-Entropy Loss
A loss function commonly used in classification tasks.
Mean Squared Error (MSE)
A loss function commonly used in regression tasks.
Precision
A metric that measures the accuracy of positive predictions.
Recall
A metric that measures the ability of a model to find all the relevant cases (true positives).
F1 Score
A metric that combines precision and recall into a single number.
ROC Curve
A graphical plot that illustrates the diagnostic ability of a binary classifier system.
AUC (Area Under the Curve)
A metric that measures the entire two-dimensional area underneath the entire ROC curve.
Confusion Matrix
A table used to describe the performance of a classification model.
Ensemble Learning
A technique that combines multiple models to improve the overall performance.