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12:17, 19 March 2023 Walle talk contribs created page Forget gate (Created page with "{{see also|Machine learning terms}} ==Forget Gate in Machine Learning== The '''forget gate''' is an essential component in machine learning models, particularly in Long Short-Term Memory (LSTM) neural networks. The primary function of the forget gate is to control the flow of information, enabling the network to learn long-term dependencies by regulating which information to retain or discard from the previous time step. This capability is crucial for sequence-to-sequenc...")
12:17, 19 March 2023 Walle talk contribs created page Exploding gradient problem (Created page with "{{see also|Machine learning terms}} ==Exploding Gradient Problem== The exploding gradient problem is a phenomenon encountered in the training of certain types of artificial neural networks, particularly deep networks and recurrent neural networks (RNNs). This problem occurs when the gradients of the loss function with respect to the model's parameters grow exponentially during the backpropagation process, leading to unstable learning dynamics and suboptimal model per...")
12:17, 19 March 2023 Walle talk contribs created page Divisive clustering (Created page with "{{see also|Machine learning terms}} ==Divisive Clustering== Divisive clustering, also referred to as "top-down" clustering, is a hierarchical clustering method employed in machine learning and data analysis. It involves recursively partitioning a dataset into smaller subsets, where each subset represents a cluster. This process starts with a single cluster encompassing all data points and proceeds by iteratively dividing the clusters until a certain stopping criterion is...")
12:17, 19 March 2023 Walle talk contribs created page Clustering (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Clustering''' is a technique in the field of machine learning and data mining that involves the grouping of similar data points or objects into clusters, based on some form of similarity or distance metric. The goal of clustering is to identify underlying patterns or structures in data, enabling efficient data representation, classification, and interpretation. Clustering is an unsupervised learning method,...")
12:16, 19 March 2023 Walle talk contribs created page Centroid (Created page with "{{see also|Machine learning terms}} ==Centroid in Machine Learning== The '''centroid''' is a central concept in machine learning, particularly in the realm of clustering algorithms. It is a geometrical point that represents the average of all data points in a particular cluster or group. Centroids are used to calculate the similarity or distance between data points, which helps in grouping similar data points together and separating dissimilar ones. ===Definition=== In...")
12:16, 19 March 2023 Walle talk contribs created page Centroid-based clustering (Created page with "{{see also|Machine learning terms}} ==Introduction== Centroid-based clustering is a class of machine learning algorithms that group data points into clusters based on the similarity of their features. These algorithms rely on the computation of centroids, which represent the central points of clusters in the feature space. The most well-known centroid-based clustering algorithm is the K-means algorithm. ==Centroid-based Clustering Algorithms== Centroid-based clu...")
12:15, 19 March 2023 Walle talk contribs created page RNN (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, '''Recurrent Neural Networks''' ('''RNNs''') are a class of artificial neural networks that are designed to process sequences of data. RNNs have gained significant popularity in recent years, particularly for tasks involving natural language processing, time series analysis, and speech recognition. Unlike traditional feedforward neural networks, RNNs possess a unique architecture t...")
12:13, 19 March 2023 Walle talk contribs created page Long Short-Term Memory (LSTM) (Created page with "{{see also|Machine learning terms}} ==Introduction== Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to address the limitations of traditional RNNs in learning long-term dependencies. LSTM networks were introduced by Hochreiter and Schmidhuber in 1997<ref name="Hochreiter1997">{{Cite journal|last1=Hochreiter|first1=Sepp|last2=Schmidhuber|first2=Jürgen|title=Long short-term memory|journal=Neural Computation|date=1997|volume...")
12:13, 19 March 2023 Walle talk contribs created page LSTM (Created page with "{{see also|Machine learning terms}} ==Introduction== Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that is specifically designed to handle long-range dependencies in sequential data. It was first introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997 to address the limitations of traditional RNNs, such as the vanishing gradient problem. LSTMs have since become a popular choice for various applications in machine lea...")
06:24, 19 March 2023 Walle talk contribs created page Trajectory (Created page with "{{see also|Machine learning terms}} ==Trajectory in Machine Learning== Trajectory in machine learning refers to the sequence of decisions, actions, and states that a model undergoes as it learns to solve a particular problem. The concept of trajectory is especially important in the context of reinforcement learning and optimization algorithms, where an agent iteratively refines its knowledge and actions in order to achieve better performance. ===Reinforcement Le...")
06:24, 19 March 2023 Walle talk contribs created page Termination condition (Created page with "{{see also|Machine learning terms}} ==Termination Condition in Machine Learning== In the field of machine learning, a termination condition, also known as stopping criterion, refers to a set of predefined criteria that determines when an optimization algorithm should cease its search for the optimal solution. Termination conditions are essential to prevent overfitting, underfitting, and excessive computational resources consumption. They help ensure that the learning...")
06:24, 19 March 2023 Walle talk contribs created page Target network (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''target network''' is a critical component of certain algorithms, primarily used to improve the stability of learning processes. It is predominantly associated with reinforcement learning methods, such as Deep Q-Networks (DQN). This article discusses the purpose and significance of target networks, along with the principles guiding their function and their role in stabilizing l...")
06:24, 19 March 2023 Walle talk contribs created page Tabular Q-learning (Created page with "{{see also|Machine learning terms}} ==Introduction== Tabular Q-learning is a fundamental reinforcement learning algorithm used in the field of machine learning. It is a value-based approach that helps agents learn optimal policies through interaction with their environment. The algorithm aims to estimate the expected cumulative reward or ''value'' for each state-action pair in a discrete environment. ==Q-learning Algorithm== Q-learning is a model-free, off-polic...")
06:24, 19 March 2023 Walle talk contribs created page State (Created page with "{{see also|Machine learning terms}} ==State in Machine Learning== State in machine learning refers to the internal representation of information or data that a model uses to make decisions or predictions. In the context of machine learning, a state is a snapshot of the variables, parameters, and information at a given point in time, during the learning or inference process. This state is crucial in determining the subsequent actions or decisions made by the model. ===Ty...")
06:24, 19 March 2023 Walle talk contribs created page State-action value function (Created page with "{{see also|Machine learning terms}} ==State-Action Value Function in Machine Learning== In the field of machine learning, particularly in the area of reinforcement learning, the state-action value function, often denoted as Q(s, a), is a crucial concept that helps agents learn optimal behavior by quantifying the expected return or long-term value of taking a specific action a in a given state s. ===Definition=== The state-action value function, or Q-function, is formall...")
06:23, 19 March 2023 Walle talk contribs created page Reward (Created page with "{{see also|Machine learning terms}} ==Reward in Machine Learning== In the field of machine learning, the concept of '''reward''' plays a crucial role in the process of learning from interaction with the environment. Reward is used as a measure of success, guiding the learning process in reinforcement learning algorithms. The objective of reinforcement learning algorithms is to maximize the cumulative reward over time. This allows the learning agent to evaluate it...")
06:23, 19 March 2023 Walle talk contribs created page Return (Created page with "{{see also|Machine learning terms}} ==Return in Machine Learning== In the context of machine learning, the term "return" refers to the cumulative reward or outcome of a series of decisions or actions taken by an agent in a reinforcement learning (RL) environment. Reinforcement learning is a subfield of machine learning in which an agent learns to make decisions by interacting with an environment to achieve a certain goal, such as maximizing a reward function. The return...")
06:23, 19 March 2023 Walle talk contribs created page Replay buffer (Created page with "{{see also|Machine learning terms}} ==Introduction== In the realm of machine learning, the '''replay buffer''' is a crucial component in a specific class of algorithms known as reinforcement learning (RL). Reinforcement learning is a branch of machine learning that involves training an agent to learn an optimal behavior by interacting with its environment, where it receives feedback in the form of rewards or penalties. The replay buffer is primarily used in a cla...")
06:23, 19 March 2023 Walle talk contribs created page Reinforcement learning (RL) (Created page with "{{see also|Machine learning terms}} ==Introduction== Reinforcement learning (RL) is a subfield of machine learning that focuses on training algorithms to make decisions by interacting with an environment. The primary objective in RL is to learn an optimal behavior or strategy, often called a ''policy'', which enables an agent to maximize its cumulative reward over time. RL algorithms are characterized by the use of trial-and-error and delayed feedback, making them pa...")
06:23, 19 March 2023 Walle talk contribs created page Random policy (Created page with "{{see also|Machine learning terms}} ==Introduction== A random policy, in the context of machine learning, refers to a decision-making process where actions are selected with equal probability, regardless of the state or history of the environment. This approach is typically used as a baseline in reinforcement learning, to compare the performance of more sophisticated policies that attempt to learn the optimal strategy for a given problem. In this article, we will discuss...")
06:23, 19 March 2023 Walle talk contribs created page Landmarks (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the term "landmarks" is often used in the context of manifold learning and dimensionality reduction techniques, where the goal is to uncover the underlying structure of high-dimensional data by representing it in a lower-dimensional space. One popular method for achieving this is by using landmark-based methods, which rely on a set of carefully selected reference points (i.e., landmarks) to capture...")
06:22, 19 March 2023 Walle talk contribs created page Keypoints (Created page with "{{see also|Machine learning terms}} ==Keypoints in Machine Learning== In the field of machine learning, keypoints play an essential role in facilitating the understanding and analysis of data. These distinctive, informative points in data serve as important elements in various machine learning applications, such as image recognition, computer vision, and natural language processing. ===Definition=== Keypoints, also known as interest points or salient points, are unique...")
06:22, 19 March 2023 Walle talk contribs created page Intersection over union (IoU) (Created page with "{{see also|Machine learning terms}} ==Intersection over Union (IoU)== Intersection over Union (IoU) is a widely used metric for evaluating the performance of object detection and instance segmentation algorithms in machine learning. It measures the degree of overlap between two bounding boxes or shapes, often representing the predicted output and the ground truth. IoU is particularly important in tasks such as object detection, semantic segmentation, and instance segment...")
06:22, 19 March 2023 Walle talk contribs created page Image recognition (Created page with "{{see also|Machine learning terms}} ==Introduction== Image recognition, also referred to as Computer Vision or object recognition, is a subfield of Machine Learning and Artificial Intelligence that deals with the ability of a computer system or model to identify and classify objects or features within digital images. The primary goal of image recognition is to teach machines to emulate the human visual system, allowing them to extract useful information from...")
06:22, 19 March 2023 Walle talk contribs created page Downsampling (Created page with "{{see also|Machine learning terms}} ==Introduction== Downsampling is a technique used in machine learning and signal processing to reduce the amount of data being processed. It involves systematically selecting a smaller subset of data points from a larger dataset, thereby reducing its size and complexity. Downsampling can be applied in various contexts, such as image processing, time series analysis, and natural language processing, among others. The primary goal of dow...")
06:22, 19 March 2023 Walle talk contribs created page Depthwise separable convolutional neural network (sepCNN) (Created page with "{{see also|Machine learning terms}} ==Depthwise Separable Convolutional Neural Network (SepCNN)== Depthwise Separable Convolutional Neural Networks (SepCNNs) are a variant of Convolutional Neural Networks (CNNs) designed to reduce computational complexity and memory usage while preserving performance in various computer vision tasks. SepCNNs achieve this by factorizing the standard convolution operation into two separate steps: depthwise convolution and pointwise con...")
06:22, 19 March 2023 Walle talk contribs created page Data augmentation (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, ''data augmentation'' refers to the process of expanding the size and diversity of a training dataset by applying various transformations and manipulations. The primary goal of data augmentation is to improve the generalization capabilities of machine learning models, thus enhancing their performance on unseen data. This article delves into the principles, techniques, and applicati...")
06:22, 19 March 2023 Walle talk contribs created page Convolutional operation (Created page with "{{see also|Machine learning terms}} ==Convolutional Operation in Machine Learning== The convolutional operation, often used in the context of Convolutional Neural Networks (CNNs), is a core element in modern machine learning techniques for image and signal processing. It involves the application of mathematical functions known as ''convolutions'' to input data, enabling the extraction of important features, patterns, and structures from raw data. This operation h...")
06:21, 19 March 2023 Walle talk contribs created page Convolutional neural network (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''convolutional neural network''' (CNN) is a type of artificial neural network specifically designed for processing grid-like data, such as images, speech signals, and time series data. CNNs have achieved remarkable results in various tasks, particularly in the field of image and speech recognition. The architecture of CNNs is inspired by the organization of the animal visual cortex and consists of multiple layers o...")
06:21, 19 March 2023 Walle talk contribs created page Convolutional layer (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a '''convolutional layer''' is a key component of Convolutional Neural Networks (CNNs) that specializes in processing and analyzing grid-like data structures, such as images. It is designed to automatically learn and detect local patterns and features through the use of convolutional filters. These filters, also known as kernels, are applied to the input data in a sliding-window manner, ena...")
06:21, 19 March 2023 Walle talk contribs created page Convolutional filter (Created page with "{{see also|Machine learning terms}} ==Convolutional Filters in Machine Learning== A '''convolutional filter''' (also known as a '''kernel''' or '''feature detector''') is a fundamental component of Convolutional Neural Networks (CNNs), a class of deep learning models specifically designed for processing grid-like data, such as images and time-series data. Convolutional filters are used to perform a mathematical operation called '''convolution''' on input data to dete...")
06:21, 19 March 2023 Walle talk contribs created page Convolution (Created page with "{{see also|Machine learning terms}} ==Introduction== Convolution is a mathematical operation widely used in the field of machine learning, especially in the domain of deep learning and convolutional neural networks (CNNs). The operation involves the element-wise multiplication and summation of two matrices or functions, typically an input matrix (or image) and a kernel (or filter). The primary purpose of convolution is to extract features from the input data,...")
06:21, 19 March 2023 Walle talk contribs created page Bounding box (Created page with "{{see also|Machine learning terms}} ==Bounding Box in Machine Learning== ===Definition=== A '''bounding box''' is a rectangular box used in machine learning and computer vision to represent the spatial extent of an object within an image or a sequence of images. It is generally defined by the coordinates of its top-left corner and its width and height. Bounding boxes are widely employed in object detection, localization, and tracking tasks, where the objective is...")
06:21, 19 March 2023 Walle talk contribs created page MNIST (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Modified National Institute of Standards and Technology (MNIST)''' dataset is a large collection of handwritten digits that has been widely used as a benchmark for evaluating the performance of various machine learning algorithms, particularly in the field of image recognition and computer vision. MNIST, introduced by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges in 1998, has played a pivot...")
21:57, 18 March 2023 Walle talk contribs created page Wisdom of the crowd (Created page with "{{see also|Machine learning terms}} ==Wisdom of the Crowd in Machine Learning== The ''Wisdom of the Crowd'' is a phenomenon that refers to the collective intelligence and decision-making ability of a group, which often leads to more accurate and reliable outcomes than individual judgments. In the context of machine learning, this concept is employed to improve the performance of algorithms by aggregating the predictions of multiple models, a technique commonly known as [...")
21:57, 18 March 2023 Walle talk contribs created page Variable importances (Created page with "{{see also|Machine learning terms}} ==Variable Importance in Machine Learning== Variable importance, also referred to as feature importance, is a concept in machine learning that quantifies the relative significance of individual variables, or features, in the context of a given predictive model. The primary goal of assessing variable importance is to identify and understand the most influential factors in a model's decision-making process. This information can be us...")
21:57, 18 March 2023 Walle talk contribs created page Threshold (for decision trees) (Created page with "{{see also|Machine learning terms}} ==Threshold in Decision Trees== In the field of machine learning, a decision tree is a widely used model for representing hierarchical relationships between a set of input features and a target output variable. The decision tree is composed of internal nodes, which test an attribute or feature, and leaf nodes, which represent a class or output value. The threshold is a critical parameter in decision tree algorithms that determines...")
21:56, 18 March 2023 Walle talk contribs created page Splitter (Created page with "{{see also|Machine learning terms}} ==Splitter in Machine Learning== A '''splitter''' in the context of machine learning refers to a method or technique used to divide a dataset into subsets, typically for the purposes of training, validation, and testing. The process of splitting data helps to prevent overfitting, generalizes the model, and provides a more accurate evaluation of a model's performance. Various techniques exist for splitting data, such as k-fold cross-val...")
21:56, 18 March 2023 Walle talk contribs created page Split (Created page with "{{see also|Machine learning terms}} ==Overview== In machine learning, the term ''split'' generally refers to the process of dividing a dataset into two or more non-overlapping parts, typically for the purposes of training, validation, and testing a machine learning model. These distinct subsets enable the evaluation and fine-tuning of model performance, helping to prevent overfitting and allowing for an unbiased estimation of the model's ability to generalize to unse...")
21:56, 18 March 2023 Walle talk contribs created page Shrinkage (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Shrinkage''' in machine learning is a regularization technique that aims to prevent overfitting in statistical models by adding a constraint or penalty to the model's parameters. Shrinkage methods reduce the complexity of the model by pulling its coefficient estimates towards zero, leading to more robust and interpretable models. Popular shrinkage methods include Ridge Regression and Lasso Regression. ==Shrinka...")
21:56, 18 March 2023 Walle talk contribs created page Sampling with replacement (Created page with "{{see also|Machine learning terms}} ==Sampling with Replacement in Machine Learning== In machine learning, sampling with replacement refers to a statistical technique used for selecting samples from a given dataset or population during the process of model training or evaluation. This method allows for a sample to be selected multiple times, as each time it is drawn, it is returned to the pool of possible samples. In this article, we will discuss the implications of samp...")
21:56, 18 March 2023 Walle talk contribs created page Root (Created page with "{{see also|Machine learning terms}} ==Root in Machine Learning== The term "root" in machine learning may refer to different concepts, depending on the context in which it is being used. Two of the most common meanings are related to decision trees and the root mean square error (RMSE) in regression models. ===Decision Trees=== In the context of decision trees, the root refers to the starting point of the tree, where the first split or decision is made. Decision trees ar...")
21:56, 18 March 2023 Walle talk contribs created page Random forest (Created page with "{{see also|Machine learning terms}} ==Introduction== Random Forest is a versatile and powerful ensemble learning method used in machine learning. It is designed to improve the accuracy and stability of predictions by combining multiple individual decision trees, each of which is trained on a random subset of the available data. This technique helps to overcome the limitations of a single decision tree, such as overfitting and high variance, while preserving the b...")
21:55, 18 March 2023 Walle talk contribs created page Policy (Created page with "{{see also|Machine learning terms}} ==Policy in Machine Learning== In the field of machine learning, a policy refers to a decision-making function that maps a given state or input to an action or output. A policy is often denoted by the symbol π (pi) and is central to the process of learning and decision-making in various machine learning algorithms, particularly in the realm of reinforcement learning. ===Reinforcement Learning and Policies=== Reinforcement lea...")
21:55, 18 March 2023 Walle talk contribs created page Permutation variable importances (Created page with "{{see also|Machine learning terms}} ==Permutation Variable Importance== Permutation Variable Importance (PVI) is a technique used in machine learning to evaluate the importance of individual features in a predictive model. This method estimates the impact of a specific feature on the model's predictive accuracy by assessing the changes in model performance when the values of that feature are permuted randomly. The main advantage of PVI is its applicability to a wide...")
21:55, 18 March 2023 Walle talk contribs created page Greedy policy (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and reinforcement learning, a '''greedy policy''' is a decision-making strategy that selects the action with the highest immediate value or reward, without considering the long-term consequences or future states. This approach can be effective in specific scenarios, but may fail to achieve optimal solutions in complex environments. This article will discuss the concept of greedy policy,...")
21:55, 18 March 2023 Walle talk contribs created page Experience replay (Created page with "{{see also|Machine learning terms}} ==Introduction== Experience Replay is a technique used in machine learning, particularly in reinforcement learning, to improve the efficiency and stability of the learning process. It is widely used in algorithms such as Deep Q-Network (DQN), Asynchronous Advantage Actor-Critic (A3C), and other deep reinforcement learning methods. Experience Replay allows the agent to store past experiences in a memory buffer and then reuse the...")
21:55, 18 March 2023 Walle talk contribs created page Epsilon greedy policy (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Epsilon-Greedy Policy''' is a widely used exploration-exploitation strategy in Reinforcement Learning (RL) algorithms. It helps balance the decision-making process between exploring new actions and exploiting the knowledge acquired thus far in order to maximize the expected cumulative rewards. ==Exploration and Exploitation Dilemma== In the context of RL, an agent interacts with an environment and learns an...")
21:55, 18 March 2023 Walle talk contribs created page Episode (Created page with "{{see also|Machine learning terms}} ==Episode in Machine Learning== An '''episode''' in machine learning refers to a sequence of steps or interactions that an agent goes through within an environment. It is a fundamental concept in the field of Reinforcement Learning (RL), where the learning process relies on trial and error. The term "episode" describes the process from the initial state until a termination condition is reached, often involving the completion of a t...")
21:54, 18 March 2023 Walle talk contribs created page Environment (Created page with "{{see also|Machine learning terms}} ==Environment in Machine Learning== The environment in machine learning is a term that refers to the contextual setting, data, and external factors that influence the training, performance, and evaluation of a machine learning algorithm. It includes a wide range of aspects, such as the type of data used, data preprocessing techniques, and the problem domain. ==Data Types and Sources== ===Structured Data=== Structured data is informati...")