Webdesk AI Glossary

Webdesk AI Glossary

A* Search: A computer algorithm used in graph traversal and pathfinding, combining the best features of uniform-cost search and pure heuristic search.

Activation Function: A function in a neural network that determines the neuron's output based on its input.

Activation Maps: Visual representations of activations produced by neurons in a given layer for a specific input.

Actor-Critic Method: A type of reinforcement learning algorithm that uses both policy and value functions to improve its predictions.

Adversarial Examples: Inputs to machine learning models that are intentionally designed to cause the model to make a mistake.

Adversarial Networks: A type of model where two networks (typically a generator and a discriminator) are trained together, competing against each other.

Adversarial Training: A training method that involves modifying the input data to train the model to be robust against adversarial attacks.

Affinity Propagation: A clustering algorithm based on the concept of "message passing" between data points.

Agent: An entity that observes and acts upon its environment, aiming to achieve certain goals.

Algorithm: A rule or instruction set that computers follow for problem-solving.

AlphaGo: A computer program developed by DeepMind to play the board game Go, known for defeating a world champion.

Anomaly Detection: Identifying rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.

Artificial General Intelligence (AGI): The hypothetical ability of an artificial intelligence system to understand or learn any intellectual task that a human being can. It involves having common sense, general knowledge, and the ability to reason and make judgments like humans do.

Artificial Intelligence (AI): The simulation of human intelligence processes by computer systems, encompassing learning, reasoning, and self-correction.

Attention Maps: Visualizations that show where a neural network, especially in tasks like image classification, is 'looking' when making a decision.

Attention Mechanism: A mechanism in deep learning models that allows them to focus on specific parts of the input when producing an output.

AutoML: Automated machine learning, where the process of constructing machine learning models is automated.

Backdoor Attacks: Malicious attacks on machine learning models where the attacker introduces a backdoor to the model during training.

Backpropagation: A method for training neural networks by updating weights using the gradient of the loss function.

Bag of Words (BoW): A representation of text data where the frequency of each word is used as a feature.

Bagging: An ensemble method that creates separate subsets of the original data and uses them to generate multiple classifiers.

Batch Normalization: A technique used to increase the stability of a neural network by normalizing the input of each layer.

Batch Size: The number of training examples used in one iteration or forward/backward pass of algorithm optimization.

Bayesian Network: A probabilistic graphical model representing variables and their dependencies via a directed acyclic graph.

BERT (Bidirectional Encoder Representations from Transformers): A transformer-based model designed to understand the context of words in a sentence by considering both the left and right context in all layers.

Bias (in AI): Systematic prejudice in AI decisions or predictions due to flawed algorithmic assumptions.

Bias (Statistical): The systematic error introduced by approximating a real-world problem which does not take into account all possible factors.

Bias-Variance Tradeoff: The balance between the error due to bias (wrong assumptions) and the error due to variance (overly complex models) in machine learning models.

Bidirectional RNN: A type of RNN that processes sequences from both ends towards the center, commonly used in natural language processing.

Capsule Network: A type of neural network designed to overcome shortcomings of convolutional neural networks, particularly in handling spatial hierarchies between features.

Catastrophic Forgetting: When neural networks forget previously learned information upon learning new information.

Chatbot: A software application designed to simulate human conversation either through text or voice interaction.

Clustering: The task of grouping a set of objects so that objects in the same group are more similar to each other than to those in other groups.

Cognitive Computing: Systems imitating human cognition to provide insights, typically involving NLP and ML.

Collaborative Filtering: A method used in recommendation systems where users get recommendations based on the likes and dislikes of similar users.

Concept Drift: Situations where the statistical properties of target variables change over time, making model updates necessary.

Confusion Matrix: A table that describes the performance of a classification model by comparing actual versus predicted classifications.

Content-based Filtering: Recommendation algorithms that provide personalized recommendations by comparing content descriptions and user profiles.

Contrastive Loss: A type of loss function that encourages a neural network to produce similar or dissimilar embeddings for pairs of inputs based on their labels.

Convolution: A mathematical operation used in convolutional neural networks, applied to the input data using a convolution filter or kernel to produce a feature map.

Convolutional Neural Network (CNN): A deep learning algorithm predominantly used for image and video recognition.

Cross-Validation: A technique to assess how well the model will generalize to an independent data set.

Curriculum Learning: A training method where the model is first trained on simpler tasks, gradually increasing the task's complexity.

Data Augmentation: Techniques that increase the amount of training data by slightly altering the input data without changing its meaning or interpretation.

Data Imputation: The process of replacing missing data with substituted values.

Data Leakage: When information from the testing dataset is, in some way, used during training, often leading to overly optimistic performance metrics.

Data Mining: Uncovering patterns and knowledge from vast data amounts using ML and statistical techniques.

Data Pipeline: A set of data processing elements that manage and transform raw data into usable input for analytics or machine learning models.

Data Wrangling: The process of cleaning, structuring, and enriching raw data into a desired format for better decision-making.

Decision Tree: A flowchart-like structure wherein each node represents a test on an attribute, each branch represents the test outcome, and each leaf node represents a class label.

Deep Learning: A ML subset that employs multi-layered neural networks to analyze data factors.

Deterministic Algorithm: An algorithm that, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states.

Differential Privacy: A system that provides means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its entries.

Digital Twin: A virtual model designed to accurately reflect a physical object, system, or process.

Domain Adaptation: Techniques to adapt a machine learning model from a source domain to a different, but related, target domain.

Dropout: A regularization technique for neural networks which randomly sets a number of outputs of hidden units to zero during training.

Dropout: A regularization technique used in neural networks where a subset of neurons is randomly ignored during training.

Eager Execution: An imperative programming environment available in TensorFlow that evaluates operations immediately without building a computational graph.

Early Stopping: A form of regularization used to avoid overfitting when training a model with an iterative method, such as gradient descent.

Elman Network: A type of recurrent neural network where connections between units form a directed cycle, useful in time series prediction.

Embedding Layer: A layer in neural networks that transforms categorical data into a dense vector of fixed size.

Embedding Space: The vector space in which embeddings (like word embeddings) are positioned.

Embeddings: Representation of categorical data or text in a continuous vector space, often used in neural networks.

Ensemble Learning: Using multiple models to obtain better predictive performance than could be obtained from any of the constituent models.

Ensemble Methods: Combining predictions from multiple machine learning algorithms to produce a more robust and accurate prediction.

Entropy: A measure of randomness or unpredictability in a dataset.

Episodic Memory: Memory of specific events or experiences, as opposed to general knowledge.

Epoch: A single pass through the entire training dataset during training.

Evolutionary Algorithm: Algorithms inspired by the process of natural selection, used in optimization and search tasks.

Expert System: Computer systems that emulate decision-making abilities of a human expert.

eXplainable AI (XAI): An area in AI focused on creating transparent models that human users can understand.

Exponential Decay: A mathematical function where the decrease is proportional to the current value.

eXtreme Gradient Boosting (XGBoost): An efficient and scalable implementation of gradient boosting.

F1 Score: A measure of a test's accuracy, defined as the harmonic mean of precision and recall.

Feature Engineering: The process of creating new features or transforming existing features to improve machine learning model performance.

Feature Extraction: The process of transforming raw data into a set of characteristics (features) that are relevant for analysis or modeling.

Feature Scaling: The method used to normalize the range of independent variables or features of the data.

Feature Selection: The process of selecting a subset of relevant features to construct a model.

Feature: A measurable phenomenon property, serving as an ML input variable.

Federated Learning: A machine learning setting where the model is trained across multiple devices or servers while keeping data localized.

Feedforward Network: Neural networks wherein connections between the nodes do not form a cycle.

Few-shot Learning: Training a machine learning model using very few labeled examples of the task of interest.

Fully Connected Layer: A layer in a neural network where each neuron is connected to every neuron in the previous layer.

Fuzzy Logic: A system of logic that allows for degrees of truth, rather than just true or false.

Gated Neural Networks: Neural networks containing logic gates within their architecture, typically used to control the flow of information.

Gated Recurrent Units (GRUs): A type of recurrent neural network that can adaptively capture dependencies of different time scales.

Gaussian Mixture Model (GMM): A probabilistic model representing normally distributed subpopulations within an overall population.

Generative Adversarial Networks (GANs): ML systems where two neural networks, a generator and a discriminator, compete to refine their capabilities.

Genetic Algorithm: An optimization algorithm based on the process of natural selection, used in AI to find approximate solutions to optimization and search problems.

Gradient Clipping: A technique to prevent gradients from becoming too large, which can result in an unstable training process.

Gradient Descent: An optimization algorithm used to minimize a function iteratively.

Graph Neural Network: Neural networks designed to process data structured as graphs, capturing the relationships between nodes.

Graph Theory: A field of mathematics about graphs, which are structures used to model pairwise relations between objects.

Greedy Algorithm: An algorithmic paradigm that follows the problem-solving heuristic of making the locally optimal choice at each stage.

Grid Search: An exhaustive search method used to find the best combination of hyperparameters for a machine learning model.

GridWorld: A common environment used in reinforcement learning where an agent learns to navigate a grid to reach a goal.

Hamming Distance: A metric used to measure the difference between two strings of equal length, counting the number of positions at which the corresponding elements are different.

Hashing: The transformation of data into a fixed-size series of bytes, often used in data retrieval and for checking data integrity.

Hebb's Rule: A neuroscientific theory suggesting an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell.

Hebbian Learning: A learning rule that states that if a synapse repeatedly takes part in firing the postsynaptic cell, the strength of the synapse is selectively increased.

Heteroscedasticity: A situation where the variability of a variable is unequal across different values of another variable, typically seen in regression analysis.

Heuristic Optimization: Techniques that use heuristic methods to find reasonably good solutions in situations where finding the optimal solution is computationally challenging.

Heuristic Search: A search strategy that uses rules or shortcuts to produce good-enough solutions to complex problems more quickly.

Heuristic: A problem-solving approach using practical methods to find an optimal solution.

Hierarchical Clustering: A method of cluster analysis that builds a hierarchy of clusters either by a bottom-up or top-down approach.

Hopfield Network: A form of recurrent artificial neural network that serves as an associative memory with binary threshold units.

Hyperbolic Tangent (tanh): An activation function that outputs values between -1 and 1.

Hyperparameters: Parameters in a machine learning model that are set before training starts, as opposed to parameters which are learned during training.

Image Segmentation: The process of dividing a digital image into distinct categories to simplify or change the image's representation.

Imbalanced Data: Datasets where classes are not represented equally.

Imputation: The process of replacing missing data with substituted values.

Incremental Learning: A training paradigm where the model is trained gradually, typically by being exposed to new data over time.

Inductive Reasoning: A type of reasoning where generalizations are made based on specific instances.

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

Information Bottleneck: A theory that seeks to understand the fundamental trade-off between the complexity and accuracy of representations in neural networks.

Information Retrieval: The science of extracting the relevant parts from large collections of data.

Instance-based Learning: A family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training.

Interpretability: The degree to which a machine learning model's predictions can be understood by humans.

Isocontours: Curves on which a function has a constant value, used in optimization landscapes to understand the shape of loss functions.

Isotropic: Having properties that are uniform in all directions, commonly referenced in algorithms dealing with distance or similarity.

Jaccard Similarity: A statistic used to measure similarity between finite sample sets.

Jensen-Shannon Divergence: A method to measure the similarity between two probability distributions.

Johnson Noise: The electronic noise generated by the thermal agitation of the charge carriers (usually electrons) inside an electrical conductor at equilibrium.

Johnson-Lindenstrauss Lemma: A mathematical result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space.

Joint Embeddings: Representations learned from data of multiple modalities, such as learning embeddings from both text and images.

Joint Probability Distribution: The probability distribution of two or more random variables.

Joint Probability: A statistical measure that calculates the likelihood of two events occurring together and at the same point in time.

Jupyter Notebook: An open-source web application that allows for the creation and sharing of live code, equations, visualizations, and narrative text.

Jupyter: An open-source tool for interactive computing and data analysis.

Just-In-Time Compilation: Compilation done during the execution of a program, rather than before the program is run.

K-fold Cross Validation: A technique for assessing the performance of an algorithm by training and evaluating it multiple times using different training and testing splits.

K-means: An unsupervised machine learning algorithm used for partitioning a dataset into a set of distinct, non-overlapping subgroups.

K-nearest Neighbors (KNN): An algorithm that classifies a data point based on how its neighbors are classified.

k-NN (k-Nearest Neighbors): A type of instance-based learning where the output is classified based on the majority of k nearest data points in the training dataset.

Kernel Trick: A method used in machine learning to make linear algorithms work in non-linear situations without explicitly computing the coordinates in the higher-dimensional space.

Kernel: A function used in kernel methods to compute the similarity or distance between data points.

Keyphrase Extraction: The process of extracting relevant and representative phrases from a piece of text.

Knowledge Base: A technology used to store complex structured and unstructured information used by computers.

Knowledge Discovery in Databases (KDD): The process of discovering useful knowledge from a collection of data.

Knowledge Distillation: A technique where a smaller model is trained to reproduce the behavior of a larger model (or an ensemble of models).

Knowledge Graph: A knowledge base that links data items in a structured manner, employing a graph-based structure.

Knowledge Representation: The area of AI concerned with emulating human knowledge on a computer.

Label Encoding: Converting categorical data into a form that could be provided to ML algorithms to do a better job in prediction.

Labeled Data: Data that has been tagged with one or more labels, often used in supervised learning.

Latent Dirichlet Allocation (LDA): A generative probabilistic model used for collections of discrete data such as text corpora.

Latent Semantic Analysis (LSA): A technique in NLP and information retrieval to identify relationships between a collection of documents and terms they contain.

Latent Space: The compressed representation of data in a lower-dimensional space, often the output of an encoder in architectures like autoencoders.

Leaky ReLU: A type of activation function that is defined as the positive part of the operand, allowing a small gradient when the unit is not active.

Learning Curve: A plot of the learning performance of a machine learning model over time or experience.

Learning Rate: A hyperparameter defining the adjustment step size when updating the weights in neural networks.

Learning to Rank: Techniques used in machine learning to train models for ranking tasks, commonly used in recommendation systems and search engines.

Lexical Analysis: The process of converting a sequence of characters into a sequence of tokens in NLP.

Linear Regression: A linear approach to modeling the relationship between dependent and independent variables.

Long Short-Term Memory (LSTM): A type of recurrent neural network capable of learning long-term dependencies.

Loss Function: A measure of how well a model's predictions match the true values, guiding training algorithms.

Low-Rank Approximation: A mathematical method used to approximate data by its most important components (often used in matrix factorization).

Machine Learning (ML): A subset of AI wherein computers learn from data without being explicitly programmed.

Masked Language Model (MLM): A model that is trained to predict a masked word in a sentence, often used in models like BERT.

Maximum Likelihood Estimation (MLE): A method used to estimate the parameters of a statistical model.

Mean Squared Error: A metric that measures the average squared differences between the estimated and true values.

Meta-learning: Algorithms that learn from multiple tasks and use that learning to perform new, unseen tasks.

Model Agnostic: A machine learning method or tool that is designed to work with any model or framework.

Model Evaluation: The process of assessing the performance of a trained machine learning model using various metrics and techniques.

Model Inversion Attack: An attack on machine learning models wherein the attacker tries to reconstruct the training input from model outputs.

Model: An ML term denoting systems trained to make predictions or decisions without using explicit instructions.

Momentum Optimization: An optimization algorithm used to accelerate gradients vectors in the right directions, thus leading to faster converging.

Momentum: A method used to accelerate the gradient vector in the right direction, thus leading to faster converging.

Monte Carlo Methods: Computational algorithms that rely on repeated random sampling to obtain numerical results for probabilistic computation.

Multi-task Learning: A machine learning approach where a model is trained to solve multiple tasks at the same time, improving generalization.

Multimodal Learning: Training models on data from multiple modalities (e.g., text and images) to improve performance and enable cross-modality predictions.

Naive Bayes: A classification technique based on applying Bayes’ theorem with the assumption of independence between every pair of features.

Natural Language Processing (NLP): An AI branch focusing on computer and human interaction through natural language.

Nearest Neighbor Search: An optimization problem to find closest points in metric spaces.

Nesterov Accelerated Gradient: A method to speed up gradient descent algorithms in optimization problems.

Neural Architecture Search (NAS): The automated process of discovering neural network architectures that perform better for a specific task.

Neural Network: Algorithms aiming to identify data relationships, simulating the human brain's structure and function.

Neural Turing Machine: A neural network model that, in addition to training data, can read and write to memory matrices, mimicking some behavior of the Turing machine.

Neurosymbolic AI: An approach that combines neural networks with symbolic logical reasoning. It aims to bridge the strengths of data-driven deep learning models like pattern recognition with the interpretability and generalization abilities of symbolic AI.

Node Embedding: Techniques used to learn continuous representations for nodes in a network.

Noise Contrastive Estimation (NCE): A method used in machine learning to approximate the likelihood in models with a large number of output classes.

Non-linear Activation Function: A function applied at each node in a neural network, introducing non-linearity to the model.

Non-parametric Model: Models that do not assume a particular form for the relationship between a dataset's features and its output.

Normal Distribution: A probability distribution characterized by a bell-shaped curve, often used in statistics and machine learning.

Normalization: The process of scaling input data to a standard range, often to help neural networks converge more quickly during training.

Object Detection: The task of detecting instances of objects of a certain class within an image.

One-hot Encoding: A process by which categorical variables are converted into a binary matrix.

Ontology: A formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts.

OpenAI: A research organization focused on creating and promoting friendly AI that benefits humanity as a whole.

Optical Character Recognition (OCR): The mechanical or electronic conversion of scanned or photographed images of handwritten, typewritten or printed text into machine-encoded text.

Optimization Landscape: A visualization or representation of how a metric (like loss) changes as the parameters of a machine learning model change.

Out-of-Bag Error: An error estimate for random forests, computed as the mean prediction error on each training sample using only trees that did not have this sample in their bootstrap.

Out-of-Core Learning: Techniques used to train machine learning models on data that cannot fit into memory at once, often by using disk storage efficiently.

Outlier: A data point that differs significantly from other observations and may arise from variability or errors.

Over-segmentation: In image processing, the result of segmenting an image into more regions than necessary.

Overfitting: A model that learns training data noise due to its complexity, performing poorly on new data.

Overparameterization: Using more parameters than needed in a model. This can allow models to fit training data more closely, but may lead to overfitting.

Parameter Tuning: The process of selecting the best parameters for a machine learning model.

Pattern Recognition: The classification of input data into objects or classes based on key features.

Perception: An AI system's capacity to interpret its surroundings by recognizing objects, speech, and text.

Perceptual Loss: A loss function that compares high-level features between the predicted and target images in a pre-trained neural network.

Pooling Layer: A layer in a convolutional neural network used to downsample the spatial dimensions of the input, commonly using max or average operations.

Pose Estimation: The task of estimating the pose of an object, typically a person, in images or videos.

Precision: The number of true positive results divided by the number of all positive results, a metric used in classification.

Predictive Modeling: Using statistical techniques to predict outcomes, often based on historical data.

Principal Component Analysis (PCA): A method used to emphasize variation and bring out strong patterns in a dataset, reducing its dimensions.

Probabilistic Graphical Model: A framework for modeling large systems of variables that have inherent uncertainty.

Probabilistic Programming: A high-level programming method to define probabilistic models and then solve these automatically.

Prompt Engineering: The practice of carefully crafting the prompts provided to AI systems in order to elicit more useful, relevant, and helpful responses.

Prototype Networks: Neural network models that are trained to produce prototypes, which are used to classify new examples.

Q-learning: A model-free reinforcement learning algorithm used to learn a policy that tells an agent what action to take under certain circumstances.

Quality Assurance in AI: Processes to ensure that AI systems operate safely, effectively, and as intended.

Quantization: The process of constraining an input from a large set to output in a smaller set, primarily in digital signal processing.

Quantum Bits (Qubits): The fundamental unit of quantum information, analogous to a bit in classical computing.

Quantum Computing: Computation using quantum mechanical phenomena, such as superposition and entanglement, with implications for AI.

Quantum Machine Learning: An interdisciplinary field that bridges quantum physics with machine learning, often making use of quantum computing.

Quantum Neural Network (QNN): A type of artificial neural network that is based on the principles of quantum mechanics.

Quasi-Newton Method: An optimization algorithm to find the local maximum or minimum of a function.

Query Expansion: A technique used in information retrieval where the query sent by the user is expanded by adding synonyms or related words.

Query Optimization: The process of finding the most efficient way to execute a given query by considering the possible query plans.

Radial Basis Function (RBF): A real-valued function whose value depends only on the distance from the origin or a fixed point.

Random Forest: An ensemble learning method that creates a 'forest' of decision trees and merges their outputs.

Random Walk: A mathematical object known as a stochastic or random process that describes a path consisting of a succession of random steps.

Recall: The number of true positive results divided by the number of positive results that should have been returned.

Recency Bias: The tendency to weigh recent events more heavily than earlier events, which can affect machine learning models if not taken into account.

Recurrent Attention Models (RAM): Neural network models that can focus on different parts of the input data at each step in the computation.

Recurrent Neural Network (RNN): Neural networks with loops, allowing information to be stored over time, extensively used for sequential data.

Recursion: A method where the solution to a problem depends on smaller instances of the same problem.

Regularization Parameter: A hyperparameter used in some machine learning models that adds a penalty to increasing model complexity.

Regularization: Techniques to prevent overfitting by adding a penalty to the loss function.

Reinforcement Learning: ML where agents decide through a system of rewards and punishments.

Reinforcement Signal: In reinforcement learning, a signal that tells the agent how well it's doing in terms of achieving its goal.

Residual Connections: Direct connections added from the input to the output of a neural network layer, as seen in architectures like ResNet.

Residual Network (ResNet): A type of neural network architecture designed to overcome the vanishing gradient problem by introducing skip connections or shortcuts.

Robotics: An AI field concerning the design, operation, and use of robots.

Saliency Maps: Visualizations that show the most important parts of an input to a neural network, often used to interpret model decisions.

Self-supervised Learning: A type of machine learning where the model generates its own supervisory signal from the input data.

Semantic Analysis: The process of drawing meaning from textual information.

Semantic Segmentation: The task of classifying each pixel in an image into a specific class.

Semantic Web: An extension of the World Wide Web that allows data to be shared and reused across applications, enterprises, and communities.

Semi-supervised Learning: A type of machine learning that uses both labeled and unlabeled data for training, often to improve model performance without the need for extensive labeling.

Sequence to Sequence Models: Models that convert sequences from one domain to sequences in another domain, often used in machine translation.

Sequential Modeling: Techniques used in machine learning to handle data where order matters, such as time series or sequences.

Softmax: A function that takes an un-normalized vector and normalizes it into a probability distribution.

Sparse Representation: Representing data with a significant number of zero-valued entries.

State Space: The collection of all possible situations or configurations of a system.

Stochastic Gradient Descent (SGD): An iterative method for optimizing an objective function with suitable smoothness properties.

Supervised Learning: ML where models are trained on labeled data, containing both input and desired output.

Swarm Intelligence: Collective behavior of decentralized, self-organized systems, inspired by natural phenomena like bird flocking or ant colonies.

Synthetic Data Generation: The use of algorithms and statistical methods to create artificial data that resembles real data.

Temporal Difference Learning: A combination of Monte Carlo and dynamic programming methods to learn the value function in reinforcement learning.

Tensors: Multi-dimensional arrays used in deep learning frameworks such as TensorFlow to represent data.

Thompson Sampling: A heuristic algorithm used for the multi-armed bandit problem, balancing exploration and exploitation.

Time Series Forecasting: The use of a model to predict future values based on previously observed values.

Time-Series Analysis: Methods used to analyze time series data in order to extract meaningful statistics and characteristics of the data.

Tokenization: The process of converting text into tokens, often words, symbols, or subwords.

Topological Data Analysis (TDA): A method that gives qualitative and quantitative information about metric spaces.

Transfer Learning: A technique where a pre-trained model is used on a new, but related task, with minor adjustments.

Transferable Features: Features in a machine learning model that can be useful for multiple tasks or in multiple domains.

Transformer Architecture: A deep learning model used primarily in the field of NLP and known for its effectiveness and efficiency.

Transformer Models: A type of deep learning model architecture primarily used in natural language processing tasks.

Triplet Loss: A loss function used for metric learning that pulls together similar items and pushes apart dissimilar items.

Turing Test: A machine intelligence measure, gauging its ability to produce indistinguishable human responses.

U-Net: A convolutional neural network designed for biomedical image segmentation, particularly known for its architecture and efficient training.

Unbiased Estimation: In statistics, an estimator is said to be unbiased if its expected value is equal to the true value of the estimated parameter.

Uncertainty Estimation: Techniques used to estimate the uncertainty of predictions in machine learning models.

Under-sampling: Reducing the number of majority class samples to balance out the class distribution, typically used in handling imbalanced datasets.

Underfitting: A statistical model that cannot adequately capture the underlying data structure.

Univariate: Analysis of a single statistical variable.

Universal Approximation Theorem: A theory that states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of R^n, under mild assumptions on the activation function.

Unrolled Network: A representation of recurrent networks where the recurrent structure is "unrolled" into a feedforward structure with repeated layers.

Unsupervised Learning: ML trained on unlabeled data, aiming to uncover hidden patterns.

Unsupervised Pre-training: Training a machine learning model on an auxiliary task without using labeled data for the main task, so that it can be fine-tuned later with less labeled data.

Upsampling: The process of increasing the resolution or size of data, such as images.

Validation: The process of evaluating the performance of an ML model on a separate dataset not used during training.

Variance Bias Tradeoff: Refers to the tradeoff between a model's ability to fit the data well (low bias) and its ability to generalize well to new data (low variance).

Variance Inflation Factor: A measure of multicollinearity in regression analysis.

Variance Reduction: Techniques used in optimization to reduce the variance of the gradient estimates to accelerate convergence.

Variance: A measure of how spread out a set of data is, often used in statistics and machine learning.

Variational Autoencoders (VAE): Generative models that can learn complex data distributions and generate new samples similar to the training data.

Variational Inference: A method in machine learning that approximates complex probability distributions by simpler, tractable distributions.

Virtual Reality (VR): A simulated experience that can be similar to or completely different from the real world, with implications for AI in creating virtual environments.

Visual Question Answering: A task where models generate answers to questions about images.

Viterbi Algorithm: A dynamic programming algorithm for finding the most likely sequence of hidden states in a hidden Markov model.

Voxel: A volume pixel, representing values in three-dimensional space, commonly used in medical imaging.

Wasserstein GAN (WGAN): A type of Generative Adversarial Network (GAN) that uses the Wasserstein distance to improve stability and performance of training.

Watson: IBM's AI platform best known for beating human champions on the game show "Jeopardy!".

Weight Decay: A regularization technique that adds a penalty to the loss function based on the magnitude of weights.

Weight Initialization: The method or strategy used to set the initial random weights of neural networks.

Weight Pruning: The process of removing certain weights in a neural network to reduce its size and computational cost.

Weight Regularization: Techniques used in neural networks to add a penalty on the magnitude of weights to prevent overfitting.

Weight Sharing: Using the same weight values across multiple network locations, common in convolutional neural networks.

Weight Tying: A technique where weights are shared among multiple layers or parts of a neural network, reducing the number of parameters and regularizing the model.

Weights: The parameters in neural networks adjusted through training to make accurate predictions.

Wide and Deep Learning: A neural network architecture that combines memorization and generalization, particularly useful in large-scale machine learning problems.

Word Embedding: The representation of words in continuous vector spaces such that semantically similar words are closer together.

Word2Vec: A group of related models used to produce word embeddings in NLP.

XAI (Explainable AI): A subfield of AI focused on creating methods and techniques for making machine learning models more interpretable and understandable.

Xavier Glorot Initialization: A method of weight initialization in neural networks to help propagate the signal deep into the network.

Xavier Initialization: A method of weights initialization in neural networks designed to keep the scale of gradients roughly the same in all layers.

XGBoost Regression: The use of the XGBoost algorithm for regression tasks, where the aim is to predict a continuous output variable.

XOR Problem: A non-linear problem that inspired the development of multi-layered neural networks.

Yann LeCun: A computer scientist known for his work on convolutional neural networks and deep learning.

YellowFin: An optimizer for deep learning that automatically adjusts its settings during training for improved performance.

YOLO (You Only Look Once): A real-time object detection system that can detect objects in images or video as a single regression problem.

YOLOv3: The third version of the YOLO (You Only Look Once) object detection algorithm, known for its speed and accuracy.

YOLOv4: The fourth version of the YOLO (You Only Look Once) object detection algorithm, known for its speed and accuracy enhancements.

YOLOv5: The fifth version of the YOLO (You Only Look Once) object detection algorithm, known for its speed and accuracy enhancements.

Z-normalization: A data normalization technique wherein values are rescaled to have a mean of 0 and a standard deviation of 1.

Z-Score Normalization: A normalization method where each feature is rescaled to have a mean of zero and a standard deviation of one.

Z-Score: A statistical measurement representing the number of standard deviations a data point is from the mean.

Zero Gradient Problem: A situation where the gradients are too close to zero, and the network refuses to learn further.

Zero Trust Architecture: A cybersecurity concept where no entity, whether outside or inside the organization's network, is trusted by default.

Zero-day Attack: A cyber-attack that occurs on the same day a weakness is discovered in software, before a fix becomes available from its creator.

Zero-padding: The addition of zeros to an input tensor, often an image, to control the spatial dimensions after convolution in a neural network.

Zero-shot Learning: A type of machine learning where the model is trained in such a way that it can make predictions for classes it has not seen during training.

Zero-shot Transfer: The ability of a machine learning model to perform a task without having seen any examples of that task during training.

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