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A method of comparing two versions of a model or Algorithm to determine which one performs better. Commonly used for web optimization and UX design.
A form of logic programming that uses abduction to generate explanations for observed phenomena.
A function that determines the output of a neuron in a Neural Network based on its input.
A machine learning technique where the model actively queries the user or another source for labeled data to improve its performance.
Specially crafted inputs designed to fool a machine learning model into making incorrect predictions or classifications. Designed to cause misclassification by adding imperceptible perturbations.
A training method that involves generating Adversarial Examples and using them to improve the robustness of a model.
An entity that can perceive its environment, make decisions, and take actions to achieve a goal.
A step-by-step procedure for solving a problem or performing a task.
The process of identifying unusual patterns or outliers in data.
The process of adding labels or metadata to data, often used for training supervised machine learning models.
A machine that can perform any intellectual task that a human can, demonstrating a broad range of problem-solving, creativity, and adaptability.
The field of computer science that focuses on creating machines that can perform tasks that typically require human intelligence.
An XML-based language used to create natural language software agents.
A computational model inspired by the structure and function of biological neural networks, used for tasks such as pattern recognition and decision-making.
A technique used in deep learning models to selectively focus on specific parts of the input data, improving the model’s ability to handle long sequences and complex relationships.
A type of neural network used for Unsupervised Learning, typically for dimensionality reduction or feature learning.
The technology that converts spoken language into written text.
An Algorithm used to train neural networks by minimizing the error between the predicted output and the actual output.
A technique used in ensemble learning where multiple models are trained on different subsets of the training data and their predictions are combined to improve overall performance.
A probabilistic graphical model that represents a set of variables and their conditional dependencies using a directed acyclic graph.
Large and complex datasets that require advanced processing techniques and technologies to analyze and extract valuable information.
A type of classification task where the goal is to separate instances into one of two classes.
An ensemble learning technique that combines the predictions of multiple weak models to create a strong model with improved performance.
A type of neural network that uses capsules to represent hierarchical relationships between different parts of an object, improving the model’s ability to recognize objects in various orientations and configurations.
A software application that uses natural language processing and artificial intelligence to simulate human-like conversation.
A supervised learning task where the goal is to assign instances to one of several predefined classes.
An unsupervised learning task where the goal is to group similar instances together based on their features.
A field of study that focuses on creating systems that can perform tasks that typically require human cognitive abilities, such as understanding natural language, recognizing patterns, and learning from experience.
A technique used in recommendation systems that predicts a user’s preferences based on the preferences of similar users.
A field of study that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos.
A type of neural network designed for processing grid-like data, such as images, using convolutional layers to detect local patterns and features.
A function that measures the difference between the predicted output and the actual output, used to guide the optimization of a machine learning model.
A technique used to assess the performance of a machine learning model by training and testing it on different subsets of the data.
The process of creating new training examples by applying transformations to existing data, such as rotation, scaling, or flipping, to improve the model’s ability to generalize.
The process of discovering patterns and relationships in large datasets using various techniques, such as machine learning, statistics, and database systems.
The process of cleaning, transforming, and organizing raw data to make it suitable for machine learning algorithms.
A multidisciplinary field that combines techniques from statistics, computer science, and domain expertise to extract insights from data.
A subfield of machine learning that focuses on using artificial neural networks with many layers to model complex patterns and representations in data.
A combination of deep learning and reinforcement learning techniques, where deep neural networks are used to represent the value or policy functions in a reinforcement learning problem.
A tree-like structure used for decision-making, where each internal node represents a decision based on a feature, and each leaf node represents an outcome or class Label.
The process of reducing the number of features or dimensions in a dataset, often by converting the data to a lower-dimensional representation, such as an embedding vector.
A Regularization technique used in neural networks, where a random subset of neurons is temporarily “dropped out” or deactivated during training to prevent overfitting.
A technique that combines the predictions of multiple models to improve overall performance and reduce the risk of overfitting.
A complete iteration through a dataset during the training of a machine learning model.
A family of optimization algorithms inspired by the process of natural selection, such as genetic algorithms and genetic programming.
A computer program that uses a knowledge base and a set of inference rules to solve problems within a specific domain.
The process of creating new features or transforming existing features to improve the performance of a machine learning model.
The process of extracting relevant information or patterns from raw data to create a more compact and informative representation.
The process of standardizing or normalizing the range of features in a dataset to improve the performance of machine learning algorithms.
A type of artificial neural network where the connections between nodes do not form cycles, and information flows in one direction from the input layer to the output layer.
The process of adjusting the weights of a pre-trained neural network to adapt it to a new task or dataset. Commonly done with transfer learning.
A form of logic that deals with approximate reasoning, allowing for partial truth values between completely true and completely false.
A type of Deep Learning model that consists of two neural networks, a generator and a discriminator, which compete against each other to generate realistic data samples.
A type of evolutionary algorithm that uses techniques inspired by natural selection, such as mutation, crossover, and selection, to optimize a solution to a problem.
An optimization algorithm used to minimize a cost function by iteratively updating the model’s parameters in the direction of the negative gradient.
A type of neural network designed for processing graph-structured data, such as social networks or chemical compounds.
A method for hyperparameter tuning that involves exhaustively searching through a predefined set of hyperparameter values to find the best combination.
A problem-solving approach that uses practical methods or shortcuts to find approximate solutions when exact solutions are not feasible or too time-consuming.
A layer of neurons in a neural network that is not directly connected to the input or output layer, used to learn intermediate representations of the data.
A parameter of a machine learning model that is not learned from the data but is set by the user or determined through a search process, such as the learning rate or the number of hidden layers.
The process of finding the best combination of hyperparameters for a machine learning model to optimize its performance.
The process of identifying objects, people, or other features in digital images using computer vision and machine learning techniques.
A dataset where the distribution of class labels is not equal, which can lead to biased or poor-performing machine learning models.
The process of finding and retrieving relevant information from a collection of documents or other sources, often using techniques from natural language processing and machine learning.
An unsupervised learning algorithm that partitions data into K clusters based on the mean distance between data points and cluster centroids.
A supervised learning algorithm that classifies instances based on the majority class label of their K nearest neighbours in the feature space.
A function used in kernel methods, such as support vector machines, to transform the input data into a higher-dimensional space, making it easier to find a linear separation between classes.
A graph-based representation of knowledge that captures the relationships between entities and concepts in a structured format.
The class or category assigned to an instance in a supervised learning task, used as the ground truth for training and evaluating machine learning models.
A generative probabilistic model used for topic modelling, which discovers the underlying topics in a collection of documents by modelling the distribution of words and topics.
A technique used in natural language processing to analyze the relationships between words and documents by creating a low-dimensional representation of the term-document matrix.
A hyperparameter that controls the step size or update rate of the model’s parameters during optimization, such as Gradient Descent.
A statistical method for modelling the relationship between a dependent variable and one or more independent variables, assuming a linear relationship.
A statistical method for binary classification that models the probability of an instance belonging to a certain class using the logistic function.
A type of recurrent neural network designed to learn and remember long-term dependencies in sequential data.
A function that measures the difference between the predicted output and the actual output, used to guide the optimization of a machine learning model.
A subfield of artificial intelligence that focuses on developing algorithms that can learn from and make predictions or decisions based on data.
The process of automatically translating text from one language to another using natural language processing and machine learning techniques.
A stochastic model that describes a sequence of events, where the probability of each event depends only on the state of the previous event.
A mathematical framework for modelling decision-making problems in which an Agent interacts with an environment to achieve a goal, taking into account the uncertainty of the environment and the agent’s actions.
The process of choosing the best machine learning model for a given task based on its performance on a validation set or other evaluation criteria.
A search algorithm used in decision-making problems, such as game playing, that combines the precision of tree search with the generality of random sampling.
A type of feedforward artificial neural network that consists of multiple layers of neurons, including an input layer, one or more hidden layers, and an output layer.
A field of study that focuses on enabling computers to understand, interpret, and generate human language.
A subfield of natural language processing that focuses on enabling computers to understand the meaning and context of human language.
A computational model inspired by the structure and function of biological neural networks, used for tasks such as pattern recognition and decision-making.
The process of identifying and locating objects in an image or video using computer vision and machine learning techniques.
The process of identifying the class or category of an object in an image or video using computer vision and machine learning techniques.
A method for representing categorical variables as binary vectors, where each category is represented by a vector with a single 1 and the rest of the elements set to 0.
A formal representation of knowledge in a specific domain, consisting of a set of concepts, their attributes, and the relationships between them.
A situation where a machine learning model learns to perform well on the training data but does not generalize well to new, unseen data.
A simple type of artificial neuron that computes a weighted sum of its inputs and applies an Activation Function to produce an output.
A dimensionality reduction technique that transforms data to a lower-dimensional representation by projecting it onto the principal components, which are the directions of maximum variance in the data.
A model-free reinforcement learning algorithm that learns the optimal action-value function by iteratively updating the estimated Q-values based on the agent’s experiences.
An ensemble learning method that constructs multiple decision trees and combines their predictions to improve overall performance and reduce the risk of overfitting.
A type of neural network designed for processing sequential data, with connections between neurons forming directed cycles that allow the network to maintain a hidden state over time.
A type of machine learning where an Agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties, and optimizing its actions to maximize the cumulative reward.
A technique used in machine learning to prevent overfitting by adding a penalty term to the loss function, which encourages the model to have simpler or sparser representations.
A supervised learning task where the goal is to predict a continuous target variable based on input features.
A popular activation function used in neural networks, defined as the positive part of its input (i.e., max(0, x)).
A type of deep neural network that uses skip connections or shortcuts to jump over some layers, allowing the network to learn residual functions and mitigate the vanishing gradient problem.
A type of generative stochastic neural network that can learn a probability distribution over its set of inputs.
The field of study that focuses on the design, construction, operation, and application of robots, often using techniques from artificial intelligence and computer vision.
A mathematical function that maps input values to the range (0, 1), often used as an activation function in neural networks.
A function that normalizes a vector of real numbers into a probability distribution, often used in the output layer of a Neural Network for multi-class classification tasks.
A variant of gradient descent that updates the model’s parameters using a random subset of the data, which can lead to faster convergence and better generalization.
Data that is organized into a specific format or schema, such as tables, graphs, or trees, making it easier to process and analyze.
A type of machine learning where a model is trained on a labeled dataset, learning to map input features to output labels.
A supervised learning algorithm used for classification and regression tasks that finds the optimal hyperplane or decision boundary that separates the data into different classes.
A field of study that focuses on the collective behaviour of decentralized, self-organized systems, often inspired by the behaviour of social insects, such as ants or bees.
A dimensionality reduction technique that is particularly well-suited for visualizing high-dimensional data in a low-dimensional space, such as 2D or 3D.
An open-source machine learning library developed by Google, used for building and training deep learning models.
A technique in machine learning where a pre-trained model is adapted to a new task or domain by Fine-tuning its parameters or reusing some of its layers.
A type of deep learning model that uses self-attention mechanisms to process input data in parallel, rather than sequentially, making it more efficient for handling long sequences and complex relationships.
A type of machine learning where a model learns to find patterns or structures in unlabeled data, such as clustering or dimensionality reduction.
A subset of the data used to evaluate the performance of a machine learning model during training, helping to tune hyperparameters and prevent Overfitting.
A type of generative neural network that learns to encode and decode data by optimizing a lower bound on the log-likelihood of the data, while also imposing a regularization term that encourages the learned representations to be smooth and continuous.
A dense vector representation of words that captures their semantic meaning and relationships, often used as input for natural language processing tasks.
An open-source library that provides an efficient and scalable implementation of gradient boosted decision trees, used for classification and regression tasks.
A real-time object detection algorithm that divides an image into a grid and predicts bounding boxes and class probabilities for each grid cell, enabling fast and accurate object detection.