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15:46, 19 March 2023 Walle talk contribs created page Similarity measure (Created page with "{{see also|Machine learning terms}} ==Similarity Measure in Machine Learning== A '''similarity measure''' is a metric used in machine learning to quantify the degree of resemblance between two objects or data points. Similarity measures are essential for many machine learning tasks, such as clustering, classification, and recommender systems. These metrics facilitate the identification of similar instances and the organization of data into meaningful grou...")
15:46, 19 March 2023 Walle talk contribs created page K-median (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''k-median''' algorithm is a popular unsupervised learning technique in the field of machine learning and data science. It is a variant of the well-known k-means clustering algorithm, which aims to partition a set of data points into ''k'' distinct clusters, where each data point belongs to the cluster with the nearest mean. The k-median algorithm, on the other hand, seeks to minimize the sum of distan...")
15:46, 19 March 2023 Walle talk contribs created page K-means (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and data analysis, '''k-means''' is an unsupervised clustering algorithm that partitions a dataset into '''k''' distinct clusters. The algorithm aims to minimize the sum of squared distances between the data points and the centroids of their corresponding clusters. It is widely used for a variety of applications such as pattern recognition, image segmentation, and customer segmentation....")
15:46, 19 March 2023 Walle talk contribs created page Convex optimization (Created page with "{{see also|Machine learning terms}} ==Introduction== Convex optimization is a subfield of mathematical optimization that deals with the minimization (or maximization) of convex functions over convex sets. In the context of machine learning, convex optimization plays a crucial role in finding the best model parameters, given a particular training dataset and a loss function. This field has gained significant attention in recent years, as it provides reliable and efficient...")
15:45, 19 March 2023 Walle talk contribs created page Convex function (Created page with "{{see also|Machine learning terms}} ==Definition== A '''convex function''' is a type of function that has particular mathematical properties, which are especially useful in the field of machine learning. Formally, a function ''f'' : ''R^n'' → ''R'' is called convex if, for all points ''x'' and ''y'' in its domain and for any scalar ''t'' in the range of 0 ≤ ''t'' ≤ 1, the following inequality holds: f(tx + (1 - t)y) ≤ tf(x) + (1 - t)f(y) This property ensur...")
15:45, 19 March 2023 Walle talk contribs created page Convenience sampling (Created page with "{{see also|Machine learning terms}} ==Introduction== Convenience sampling, also known as opportunity sampling or accidental sampling, is a non-probability sampling method utilized in various fields, including machine learning and statistics. It involves selecting a sample based on its accessibility and ease of collection, rather than following a random sampling process. Despite its limitations, convenience sampling can serve as a useful preliminary step for exploratory r...")
15:45, 19 March 2023 Walle talk contribs created page Confirmation bias (Created page with "{{see also|Machine learning terms}} ==Definition== Confirmation bias in machine learning refers to the phenomenon where a learning algorithm tends to prioritize or overfit data that confirms its pre-existing beliefs or hypotheses, while ignoring or underfitting data that contradicts them. This type of bias may arise from various sources, such as biased training data, biased model initialization, or biased model architectures. The existence of confirmation bias in machine...")
15:45, 19 March 2023 Walle talk contribs created page Collaborative filtering (Created page with "{{see also|Machine learning terms}} ==Introduction== Collaborative filtering (CF) is a widely-used technique in the field of machine learning, specifically in the domain of recommendation systems. It leverages the behavior or preferences of users within a community to make personalized recommendations for individual users. Collaborative filtering can be broadly categorized into two main approaches: user-based and item-based collaborative filtering. ==User-based Collabor...")
15:45, 19 March 2023 Walle talk contribs created page Co-adaptation (Created page with "{{see also|Machine learning terms}} ==Co-adaptation in Machine Learning== Co-adaptation is a phenomenon in machine learning that occurs when a model becomes too reliant on certain features or training examples, leading to a decrease in generalization performance. This article provides an overview of co-adaptation in the context of machine learning, its implications, and methods for mitigating its effects. ===Definition and Causes=== In machine learning, co-adaptation re...")
15:45, 19 March 2023 Walle talk contribs created page Checkpoint (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''checkpoint''' refers to a snapshot of the current state of a model during the training process. Checkpoints are primarily used for saving the model's weights and architecture, and sometimes additional information such as learning rates and optimizer states, at regular intervals or after a specified number of iterations. This allows the training process to be resumed from a previous state in...")
15:44, 19 March 2023 Walle talk contribs created page Candidate sampling (Created page with "{{see also|Machine learning terms}} ==Candidate Sampling in Machine Learning== Candidate sampling is a method used in machine learning, particularly in the context of training large-scale models. It is an optimization technique that reduces the computational complexity of learning algorithms by approximating the gradient of the loss function. In this section, we will explore the concept of candidate sampling, its motivation, and its applications in machine learning. ===...")
15:44, 19 March 2023 Walle talk contribs created page Candidate generation (Created page with "{{see also|Machine learning terms}} ==Candidate Generation in Machine Learning== Candidate generation is a critical process in machine learning (ML) that involves identifying a set of potential solutions, or "candidates," to solve a specific problem. This process is commonly used in various ML tasks, such as recommender systems, pattern mining, and search algorithms. The main goal of candidate generation is to efficiently explore the solution space and reduce...")
15:44, 19 March 2023 Walle talk contribs created page Calibration layer (Created page with "{{see also|Machine learning terms}} ==Calibration Layer in Machine Learning== Calibration is a crucial aspect of machine learning, specifically in the context of probabilistic models. The calibration layer refers to an additional component in a machine learning model designed to adjust the predicted probabilities so that they better match the true probabilities of the outcomes. This article discusses the concept of calibration in machine learning, its importance, and the...")
15:44, 19 March 2023 Walle talk contribs created page Broadcasting (Created page with "{{see also|Machine learning terms}} ==Broadcasting in Machine Learning== Broadcasting is a fundamental concept in machine learning, particularly in the context of linear algebra operations and array manipulation. It is used to perform element-wise operations on arrays of different shapes and dimensions without the need for explicit loops or reshaping, making it both computationally efficient and memory efficient. Broadcasting is widely implemented in various machine lear...")
15:44, 19 March 2023 Walle talk contribs created page Boosting (Created page with "{{see also|Machine learning terms}} ==Introduction== Boosting is an ensemble technique in machine learning that aims to improve the predictive accuracy of a model by combining the outputs of multiple weak learners. The concept of boosting was first introduced by Schapire (1990) and Freund (1995), who later developed the widely used algorithm AdaBoost (Adaptive Boosting) with Schapire in 1997. Boosting algorithms work by iteratively adjusting the weights of data point...")
15:44, 19 March 2023 Walle talk contribs created page Bias (math) or bias term (Created page with "{{see also|Machine learning terms}} ==Definition== In the context of Machine Learning, '''bias''' is a term used to describe the systematic error that a learning algorithm may have when trying to predict the true underlying relationship between input features and output targets. The '''bias term''', also known as the '''intercept''' or simply '''bias''', is a constant value added to the prediction function of a model, usually denoted as ''b'' or ''w₀'', which helps...")
15:43, 19 March 2023 Walle talk contribs created page Batch normalization (Created page with "{{see also|Machine learning terms}} ==Introduction== Batch normalization (BN) is a widely-used technique in machine learning and deep learning that helps to stabilize and accelerate the training of deep neural networks. It was first introduced by Sergey Ioffe and Christian Szegedy in their 2015 paper titled "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" 1. The primary goal of batch normalization is to address th...")
15:43, 19 March 2023 Walle talk contribs created page Baseline (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, the term '''baseline''' refers to a simple or naïve model that serves as a reference point against which the performance of more sophisticated models is compared. Establishing a baseline is essential in machine learning tasks, as it provides a starting point to measure the improvement achieved by more advanced techniques. Baselines can be established using simple statistical measures, random cho...")
15:43, 19 March 2023 Walle talk contribs created page Average precision (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Average precision''' is a widely used evaluation metric in the field of machine learning and information retrieval. It measures the effectiveness of an algorithm in retrieving relevant instances within a ranked list of items. This metric is particularly useful in scenarios where the list of items contains a large number of irrelevant items, such as in search engines and recommender systems. In this article, we w...")
15:43, 19 March 2023 Walle talk contribs created page Cloud TPU (Created page with "{{see also|Machine learning terms}} ==Introduction== Cloud TPU (Tensor Processing Unit) is a specialized hardware accelerator designed by Google for machine learning tasks, specifically tailored to accelerate the training and inference of TensorFlow models. It was introduced in 2017 and has since become an integral part of Google's Cloud Platform for researchers, developers, and businesses that require powerful and efficient processing capabilities for th...")
15:43, 19 March 2023 Walle talk contribs created page Bayesian optimization (Created page with "{{see also|Machine learning terms}} ==Introduction== Bayesian optimization is a global optimization technique in the field of machine learning, primarily used for hyperparameter tuning and expensive black-box optimization problems. The approach is based on the principles of Bayesian inference, where prior knowledge is updated with observed data to make better predictions about the unknown function. Bayesian optimization has been widely used in various applications, inclu...")
15:43, 19 March 2023 Walle talk contribs created page Bayesian neural network (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Bayesian neural network''' (BNN) is a probabilistic model in the field of machine learning that combines the flexibility and learning capabilities of artificial neural networks (ANNs) with the principles of Bayesian inference to make predictions and perform decision-making under uncertainty. BNNs extend ANNs by incorporating probability distributions over the weights and biases, enabling the network to...")
12:25, 19 March 2023 Walle talk contribs created page Vanishing gradient problem (Created page with "{{see also|Machine learning terms}} ==Vanishing Gradient Problem== The '''vanishing gradient problem''' is a significant challenge encountered in training deep neural networks, particularly in the context of backpropagation and gradient-based optimization algorithms. It arises due to the exponential decay of gradients as they are back-propagated through the layers, which results in very slow learning or, in some cases, no learning at all. This issue has hinde...")
12:19, 19 March 2023 Walle talk contribs created page Translational invariance (Created page with "{{see also|Machine learning terms}} ==Translational Invariance in Machine Learning== ===Introduction=== Translational invariance is a property of certain machine learning models, specifically in the field of image and signal processing, that allows the model to recognize patterns, regardless of their location in the input data. This property is particularly important for tasks like image recognition, where the model must identify features of interest irrespective of wher...")
12:19, 19 March 2023 Walle talk contribs created page Timestep (Created page with "{{see also|Machine learning terms}} ==Timestep in Machine Learning== A '''timestep''' in the context of machine learning refers to a specific instance in time or the unit of time progression used in various types of time-dependent algorithms. This concept is particularly relevant when working with time series data, sequential data, and when developing models for tasks such as natural language processing and reinforcement learning. In these scenarios,...")
12:19, 19 March 2023 Walle talk contribs created page Subsampling (Created page with "{{see also|Machine learning terms}} ==Definition== Subsampling, also known as '''downsampling''', is a technique used in machine learning and statistics to reduce the size of a dataset by selecting a smaller representative subset of the data. This process is applied to decrease the computational complexity and memory requirements of machine learning algorithms, while maintaining the quality of the obtained results. Subsampling is especially useful when dealing wi...")
12:19, 19 March 2023 Walle talk contribs created page Stride (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''stride''' refers to a parameter that determines the step size used during the convolution or pooling process in convolutional neural networks (CNNs). Stride plays a critical role in managing the spatial dimensions of feature maps, which can directly affect the model's efficiency and computational requirements. This article will explain the concept of stride, its role in CNNs, and its impact...")
12:18, 19 March 2023 Walle talk contribs created page Spatial pooling (Created page with "{{see also|Machine learning terms}} ==Spatial Pooling in Machine Learning== Spatial pooling, also known as spatial subsampling, is a technique utilized in various machine learning algorithms, particularly in the field of Convolutional Neural Networks (CNNs). It is designed to reduce the spatial dimensions of feature maps while retaining significant information. Spatial pooling is essential in creating a more compact representation of the input data, which consequentl...")
12:18, 19 March 2023 Walle talk contribs created page Size invariance (Created page with "{{see also|Machine learning terms}} ==Size Invariance in Machine Learning== Size invariance is a property of machine learning models and algorithms that allows them to be robust to variations in the size or scale of input data. This property is particularly important in tasks such as image recognition and object detection, where the same object may appear in different sizes and scales within the input data. Achieving size invariance can greatly improve the generalization...")
12:18, 19 March 2023 Walle talk contribs created page Sequence model (Created page with "{{see also|Machine learning terms}} ==Sequence Models in Machine Learning== Sequence models in machine learning are a class of computational models that deal with data represented as sequences or time series. These models are designed to capture the underlying patterns, dependencies, and structures in sequential data, which can be critical for tasks such as natural language processing, speech recognition, and time series forecasting. ===Types of Sequence Models=== There...")
12:18, 19 March 2023 Walle talk contribs created page Rotational invariance (Created page with "{{see also|Machine learning terms}} ==Rotational Invariance in Machine Learning== Rotational invariance, in the context of machine learning, refers to the ability of a model or algorithm to recognize and accurately process data regardless of the orientation or rotation of the input. This property is particularly important in computer vision and pattern recognition tasks, where the same object or pattern can appear in different orientations within the input data. ===Back...")
12:18, 19 March 2023 Walle talk contribs created page Recurrent neural network (Created page with "{{see also|Machine learning terms}} ==Recurrent Neural Network== A '''recurrent neural network''' ('''RNN''') is a class of artificial neural network designed to model sequential data by maintaining an internal state that can persist information across time steps. RNNs are particularly effective in tasks that involve time series data or sequences, such as natural language processing, speech recognition, and time series prediction. ===Structure and Function=== Recurr...")
12:18, 19 March 2023 Walle talk contribs created page Pooling (Created page with "{{see also|Machine learning terms}} ==Pooling in Machine Learning== Pooling is a technique employed in the field of machine learning, specifically in the context of convolutional neural networks (CNNs). The primary goal of pooling is to reduce the spatial dimensions of input data, while maintaining essential features and reducing computational complexity. It is an essential component in the processing pipeline of CNNs and aids in achieving translational invariance, w...")
12:17, 19 March 2023 Walle talk contribs created page Hierarchical clustering (Created page with "{{see also|Machine learning terms}} ==Introduction== Hierarchical clustering is a method of cluster analysis in machine learning and statistics used to group similar objects into clusters based on a measure of similarity or distance between them. This approach organizes data into a tree-like structure, called a dendrogram, that represents the nested hierarchical relationships among the clusters. Hierarchical clustering can be categorized into two primary appr...")
12:17, 19 March 2023 Walle talk contribs created page Gradient clipping (Created page with "{{see also|Machine learning terms}} ==Gradient Clipping in Machine Learning== Gradient clipping is a technique employed in machine learning, specifically during the training of deep neural networks, to mitigate the effect of exploding gradients. Exploding gradients occur when the gradients of the model parameters become excessively large, leading to instabilities and impairments in the learning process. Gradient clipping aids in the regularization of the learning process...")
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...")