**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.

**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 Bayesâ€™ 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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