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22:23, 21 March 2023 Walle talk contribs created page TPU resource (Created page with "{{see also|Machine learning terms}} ==Introduction== The TPU, or Tensor Processing Unit, is a specialized type of hardware developed by Google for the purpose of accelerating machine learning tasks, particularly those involving deep learning and artificial intelligence. TPUs are designed to deliver high performance with low power consumption, making them an attractive option for large-scale machine learning applications. ==Architecture and Design== ===Overview=== Th...")
22:23, 21 March 2023 Walle talk contribs created page TPU node (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Tensor Processing Unit (TPU) node''' is a specialized hardware accelerator designed to significantly accelerate machine learning workloads. Developed by Google, TPUs are optimized for tensor processing, which is the foundational mathematical operation in various machine learning frameworks such as TensorFlow. By providing dedicated hardware for these calculations, TPUs enable faster training and inference of m...")
22:23, 21 March 2023 Walle talk contribs created page TPU master (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''TPU master''' in machine learning refers to the primary control unit of a Tensor Processing Unit (TPU), which is a specialized hardware accelerator designed to significantly speed up the execution of machine learning tasks. TPUs were developed by Google to improve the performance of deep learning algorithms and reduce their training and inference times. The TPU master coordinates the flow of data and instruc...")
22:23, 21 March 2023 Walle talk contribs created page TPU device (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Tensor Processing Unit (TPU)''' is a type of application-specific integrated circuit (ASIC) designed and developed by Google specifically for accelerating machine learning tasks. TPUs are custom-built hardware accelerators optimized to handle the computational demands of machine learning algorithms, particularly deep learning and neural networks. They provide significant performance improvements and en...")
22:23, 21 March 2023 Walle talk contribs created page TPU chip (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Tensor Processing Unit''' ('''TPU''') is a type of application-specific integrated circuit (ASIC) designed by Google specifically for accelerating machine learning workloads. TPUs are optimized for the computational demands of neural networks and are particularly efficient at performing operations with tensors, which are multi-dimensional arrays of data commonly used in machine learning applications. TPU...")
22:22, 21 March 2023 Walle talk contribs created page TPU Pod (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''TPU Pod''' is a cluster of Tensor Processing Units (TPUs) designed to accelerate high-performance computation tasks. TPUs are specialized hardware accelerators developed by Google, specifically optimized for performing tensor-based mathematical operations commonly used in machine learning and deep learning algorithms. TPU Pods allow researchers and engineers to scale up their...")
22:22, 21 March 2023 Walle talk contribs created page TPU (Created page with "{{see also|Machine learning terms}} ==Overview== A '''Tensor Processing Unit (TPU)''' is a type of application-specific integrated circuit (ASIC) developed by Google for accelerating machine learning workloads. TPUs are designed to perform tensor computations efficiently, which are the foundational operations in machine learning algorithms, particularly deep learning models. They are optimized for handling large-scale matrix operations with low precision, enabling fa...")
01:15, 21 March 2023 Walle talk contribs created page Self-supervised learning (Created page with "{{see also|Machine learning terms}} ==Introduction== Self-supervised learning (SSL) is a subfield of machine learning that focuses on learning representations of data in an unsupervised manner by exploiting the structure and inherent properties of the data itself. This approach has gained significant traction in recent years, as it enables algorithms to learn useful features from large volumes of unlabeled data, thereby reducing the reliance on labeled datasets. The lear...")
01:15, 21 March 2023 Walle talk contribs created page Selection bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Selection bias in machine learning refers to the phenomenon where the sample data used to train or evaluate a machine learning model does not accurately represent the underlying population or the target domain. This issue arises when the training data is collected or selected in a way that introduces systematic errors, which can lead to biased predictions or conclusions when the model is applied to real-world scena...")
01:15, 21 March 2023 Walle talk contribs created page Scoring (Created page with "{{see also|Machine learning terms}} ==Overview== In the field of machine learning, scoring refers to the process of evaluating a trained model's performance based on its ability to make predictions on a given dataset. The scoring process typically involves comparing the model's predictions to the actual or true values, also known as ground truth or targets. A variety of evaluation metrics are used to quantify the model's performance, with the choice of metric often d...")
01:15, 21 March 2023 Walle talk contribs created page Scikit-learn (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Scikit-learn''' is an open-source Python library designed for use in the field of machine learning. The library provides a wide range of machine learning algorithms, including those for classification, regression, clustering, dimensionality reduction, and model selection. Developed by a team of researchers and engineers, scikit-learn is built on top of the NumPy, SciPy, and matplotlib libraries,...")
01:14, 21 March 2023 Walle talk contribs created page Scaling (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, scaling refers to the process of adjusting the range of input features or data points to a uniform scale. This normalization of data is an essential pre-processing step that enhances the performance and efficiency of machine learning algorithms by addressing issues of heterogeneity and uneven distribution of features. ==Importance of Scaling in Machine Learning== Scaling is a crit...")
01:14, 21 March 2023 Walle talk contribs created page Scalar (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a ''scalar'' refers to a single numerical value that can represent a quantity or measurement. Scalars play a crucial role in many aspects of machine learning algorithms, from representing weights and biases in neural networks to serving as input features or output labels in various machine learning models. This article will cover the definition, importance, and usage of scalars in machine learn...")
01:14, 21 March 2023 Walle talk contribs created page Sampling bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Sampling bias in machine learning is a type of bias that occurs when the data used for training and testing a model does not accurately represent the underlying population. This can lead to a model that performs poorly in real-world applications, as it is not able to generalize well to the broader population. In this article, we will discuss the various causes and types of sampling bias, the consequences of samplin...")
01:14, 21 March 2023 Walle talk contribs created page Root directory (Created page with "{{see also|Machine learning terms}} ==Root Directory in Machine Learning== In the context of machine learning, the term "root directory" does not directly refer to a specific concept or technique. Instead, it is related to file and folder organization in computer systems, which is crucial for managing datasets, code, and resources for machine learning projects. In this article, we will discuss the concept of a root directory in the context of computer systems and how it...")
01:14, 21 March 2023 Walle talk contribs created page Ridge regularization (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, regularization is a technique used to prevent overfitting and improve the generalization of models by adding a penalty term to the objective function. Ridge regularization, also known as L2 regularization or Tikhonov regularization, is a specific type of regularization that adds a squared L2-norm of the model parameters to the loss function. This article discusses the underlying principles of ridge...")
01:14, 21 March 2023 Walle talk contribs created page Representation (Created page with "{{see also|Machine learning terms}} ==Introduction== Representation in machine learning refers to the method by which a model captures and encodes the underlying structure, patterns, and relationships present in the input data. A suitable representation allows the model to learn and generalize from the data effectively, enabling it to make accurate predictions or perform other tasks. Representations can be hand-crafted features, which are based on expert knowledge, o...")
01:13, 21 March 2023 Walle talk contribs created page Reporting bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Reporting bias in machine learning refers to a systematic distortion of the information used to train and evaluate machine learning models. This distortion arises when the data being used to train a model is influenced by factors that are not representative of the true underlying phenomenon. These factors can lead to an overestimation or underestimation of certain model predictions, ultimately affecting the performance...")
01:13, 21 March 2023 Walle talk contribs created page Recommendation system (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''recommendation system''' in machine learning is a type of algorithm that provides personalized suggestions or recommendations to users, typically in the context of digital platforms such as e-commerce websites, streaming services, and social media platforms. These systems leverage various techniques from the fields of machine learning, data mining, and information retrieval to identify and rank items or conten...")
01:13, 21 March 2023 Walle talk contribs created page Recall (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Recall''' is a performance metric commonly used in machine learning and information retrieval to evaluate the effectiveness of classification and retrieval models. It is particularly useful when the cost of false negatives (failing to identify positive instances) is high. This article provides an in-depth understanding of the concept of recall, its mathematical formulation, and its relation to other performa...")
01:13, 21 March 2023 Walle talk contribs created page Re-ranking (Created page with "{{see also|Machine learning terms}} ==Introduction== Re-ranking, also known as rank refinement or re-scoring, is an essential technique in machine learning that aims to improve the quality of ranked results generated by a primary ranking model. It involves using a secondary model to adjust the initial ranking produced by the primary model, based on various features and criteria. Re-ranking is widely applied in diverse fields, such as information retrieval, natu...")
01:13, 21 March 2023 Walle talk contribs created page Ranking (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, ranking refers to the process of sorting a set of items in a specific order based on their relevance, importance, or some other predefined criteria. This process has become increasingly important in a wide range of applications, such as information retrieval, recommendation systems, and natural language processing. By utilizing machine learning algorithms and models, ranking system...")
01:13, 21 March 2023 Walle talk contribs created page Rank (ordinality) (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''rank''' or '''ordinality''' refers to a specific type of data that represents a relative order or position among a set of items. Unlike continuous numerical data, which can take any value within a range, or categorical data, which consists of discrete values with no inherent order, ordinal data possesses an inherent order or ranking, but the intervals between the values are not necessarily consi...")
01:12, 21 March 2023 Walle talk contribs created page Rank (Tensor) (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the term "rank" is commonly used in the context of tensor algebra. A tensor is a mathematical object that is a generalization of scalars, vectors, and matrices, and is used to represent complex data structures in various machine learning algorithms. The rank of a tensor refers to the number of dimensions or indices required to represent the tensor. ==Tensor Basics== ===Scalars, Vectors, and Matric...")
01:12, 21 March 2023 Walle talk contribs created page Queue (Created page with "{{see also|Machine learning terms}} ==Queue in Machine Learning== Queue, in the context of machine learning, refers to the use of a data structure known as a queue to store and manage data during the processing of machine learning tasks. Queues are data structures that follow the First-In-First-Out (FIFO) principle, meaning that elements are removed from the queue in the order they were inserted. Queues can be utilized in various stages of the machine learning proces...")
01:12, 21 March 2023 Walle talk contribs created page Quantization (Created page with "{{see also|Machine learning terms}} ==Quantization in Machine Learning== Quantization is a technique utilized in machine learning and deep learning to reduce the size of models and computational resources needed for their operation. The process entails approximating the continuous values of parameters, such as weights and activations, using a smaller, discrete set of values. Quantization is particularly useful in deploying models on resource-constrained devices,...")
01:12, 21 March 2023 Walle talk contribs created page Quantile bucketing (Created page with "{{see also|Machine learning terms}} ==Introduction== Quantile bucketing, also known as quantile binning or quantile-based discretization, is a technique in machine learning and data preprocessing that aims to transform continuous numeric features into discrete categories by partitioning the data distribution into intervals, with each interval containing an equal proportion of data points. This process improves the efficiency and interpretability of certain algori...")
01:12, 21 March 2023 Walle talk contribs created page Quantile (Created page with "{{see also|Machine learning terms}} ==Quantile in Machine Learning== A '''quantile''' is a statistical concept used in machine learning, which refers to the division of a data distribution into equal intervals. These intervals represent different portions of the data distribution and are used for various statistical analyses, such as summarizing data, understanding its structure, and making inferences. ===Definition=== Formally, a quantile is defined as a value that div...")
01:12, 21 March 2023 Walle talk contribs created page Proxy (sensitive attributes) (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, '''proxy (sensitive attributes)''' refers to variables that indirectly capture information about a sensitive attribute, such as race, gender, or age, which are often used in a model to make predictions or decisions. The use of proxy variables can inadvertently lead to biased outcomes or algorithmic discrimination, even when the original sensitive attribute is not explicitly used in the model. It...")
01:12, 21 March 2023 Walle talk contribs created page Probabilistic regression model (Created page with "{{see also|Machine learning terms}} ==Probabilistic Regression Model== Probabilistic regression models are a class of machine learning techniques that predict the relationship between input features and a continuous target variable by estimating a probability distribution of the target variable. These models account for uncertainties in the predictions by providing a range of possible outcomes and their associated probabilities. Probabilistic regression models are wi...")
01:11, 21 March 2023 Walle talk contribs created page Prior belief (Created page with "{{see also|Machine learning terms}} ==Prior Belief in Machine Learning== Prior belief, also known as '''prior probability''' or simply '''prior''', is a fundamental concept in the field of machine learning, particularly in Bayesian statistics and Bayesian machine learning methods. The prior represents the initial belief or probability distribution of a model regarding the values of its parameters before any data is taken into account. This section will cover the...")
01:11, 21 March 2023 Walle talk contribs created page Preprocessing (Created page with "{{see also|Machine learning terms}} ==Introduction== Preprocessing in machine learning refers to the initial stage of preparing raw data for use in machine learning algorithms. This critical step involves transforming and cleaning the data to enhance its quality, reduce noise, and ensure its compatibility with the chosen machine learning model. By performing preprocessing, data scientists and engineers aim to improve the efficiency and accuracy of machine learning al...")
01:11, 21 March 2023 Walle talk contribs created page Predictive rate parity (Created page with "{{see also|Machine learning terms}} ==Introduction== Predictive rate parity is an important concept in the field of machine learning, particularly in the context of fairness and bias. It is a metric used to measure the fairness of a machine learning model, especially in cases where the model makes predictions for different groups within a dataset. The goal of achieving predictive rate parity is to ensure that the model's predictions are equitable across these groups, min...")
01:11, 21 March 2023 Walle talk contribs created page Predictive parity (Created page with "{{see also|Machine learning terms}} ==Predictive Parity in Machine Learning== Predictive parity, also known as test fairness, is a crucial criterion for evaluating the fairness of machine learning algorithms. It refers to the condition when the predictive accuracy of an algorithm is consistent across different demographic groups. In other words, the probability of a correct prediction should be equal among all subgroups within the population. This concept is essentia...")
01:11, 21 March 2023 Walle talk contribs created page Prediction bias (Created page with "{{see also|Machine learning terms}} ==Definition== Prediction bias refers to a systematic error in a machine learning model's predictions, where the model consistently over- or under-estimates the true value of the target variable. This phenomenon occurs when the model's predictions exhibit a persistent deviation from the actual values, leading to inaccurate and unreliable results. The presence of prediction bias can significantly impair a model's generalization capabili...")
01:11, 21 March 2023 Walle talk contribs created page Precision (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, ''precision'' is a fundamental metric used to evaluate the performance of classification models. Precision measures the accuracy of positive predictions made by a model, specifically the proportion of true positive instances among all instances classified as positive. This metric is particularly important in cases where the cost of false positives is high, such as in medical diag...")
01:10, 21 March 2023 Walle talk contribs created page Precision-recall curve (Created page with "{{see also|Machine learning terms}} ==Precision-Recall Curve in Machine Learning== In machine learning, the precision-recall curve is a graphical representation that illustrates the performance of a binary classification model. The curve is used to assess the trade-off between two important evaluation metrics: precision and recall. ===Definition of Precision and Recall=== * '''Precision''' refers to the proportion of true positive predictions out of all positive...")
01:10, 21 March 2023 Walle talk contribs created page Pre-trained model (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a pre-trained model refers to a model that has been previously trained on a large dataset and can be fine-tuned for a specific task. The motivation behind using a pre-trained model is to leverage the knowledge gained during its initial training, thus reducing the time, computational resources, and the amount of data required for training a new model from scratch. ==Pre-training Me...")
01:10, 21 March 2023 Walle talk contribs created page Pipeline (Created page with "{{see also|Machine learning terms}} ==Pipeline in Machine Learning== A '''pipeline''' in machine learning refers to a sequence of data processing and transformation steps, combined with a learning algorithm, to create a complete end-to-end workflow for training and predicting outcomes. Pipelines are essential for streamlining machine learning tasks, ensuring reproducibility and efficiency, and facilitating collaboration among data scientists and engineers. ===Preproces...")
01:10, 21 March 2023 Walle talk contribs created page Performance (Created page with "{{see also|Machine learning terms}} ==Introduction== Performance in machine learning refers to the effectiveness of a machine learning model in achieving its intended purpose, which is typically to make accurate predictions or classifications based on input data. Performance evaluation is a critical aspect of machine learning, as it helps determine the quality of a model and its suitability for a particular task. This article will discuss various aspects of performance i...")
01:10, 21 March 2023 Walle talk contribs created page Perceptron (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''perceptron''' is a type of linear classifier and an early form of artificial neural network, which was introduced by Frank Rosenblatt in 1957. Perceptrons are designed to model simple decision-making processes in machine learning, and are primarily used for binary classification tasks, where the goal is to distinguish between two possible outcomes. Although they have been largely superseded by more advanced algori...")
01:10, 21 March 2023 Walle talk contribs created page Partitioning strategy (Created page with "{{see also|Machine learning terms}} ==Partitioning Strategy in Machine Learning== In the field of machine learning, the partitioning strategy refers to the method of dividing a dataset into separate subsets to facilitate the training, validation, and testing of models. Partitioning plays a crucial role in ensuring the robustness, accuracy, and generalizability of the model when applied to real-world situations. This article explores the various partitioning strategie...")
01:09, 21 March 2023 Walle talk contribs created page Participation bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Participation bias, also known as selection bias, is a type of bias in machine learning that occurs when the training data used to develop a model is not representative of the population of interest. This can lead to a model that performs poorly on new, unseen data, as it has only learned the patterns present in the biased sample. Participation bias can be particularly problematic in applications such as medical di...")
01:09, 21 March 2023 Walle talk contribs created page Partial derivative (Created page with "{{see also|Machine learning terms}} ==Partial Derivative in Machine Learning== In machine learning, the concept of partial derivatives plays a crucial role in optimization techniques, primarily for the training and refinement of models. Partial derivatives are a mathematical concept derived from calculus and are utilized to understand how a function changes when one of its variables is altered, while keeping the other variables constant. ===Definition and Notation==...")
01:09, 21 March 2023 Walle talk contribs created page Parameter update (Created page with "{{see also|Machine learning terms}} ==Parameter Update in Machine Learning== In the field of machine learning, parameter update refers to the process of iteratively adjusting the values of a model's parameters to minimize the difference between the model's predictions and the actual outcomes. The primary objective of this process is to improve the model's performance on a given task, such as classification or regression, by reducing its error rate. ===Gradient Desce...")
01:09, 21 March 2023 Walle talk contribs created page Oversampling (Created page with "{{see also|Machine learning terms}} ==Oversampling in Machine Learning== Oversampling is a technique used in the field of machine learning to address the issue of imbalanced data by increasing the number of samples in the minority class. This process aims to achieve a balanced distribution of classes within the dataset, which ultimately leads to improved performance of machine learning algorithms. ===Imbalanced Data=== Imbalanced data occurs when the distribution o...")
01:09, 21 March 2023 Walle talk contribs created page Outliers (Created page with "{{see also|Machine learning terms}} ==Outliers in Machine Learning== In the field of machine learning, outliers are data points that deviate significantly from the majority of the other data points in a given dataset. These data points can have a substantial impact on the results and performance of machine learning algorithms, potentially leading to erroneous or misleading conclusions. This article discusses the concept of outliers, their implications in machine learning...")
01:09, 21 March 2023 Walle talk contribs created page Outlier detection (Created page with "{{see also|Machine learning terms}} ==Outlier Detection in Machine Learning== Outlier detection, also referred to as anomaly detection or novelty detection, is a process in machine learning and statistics that involves identifying data points, observations, or patterns that significantly deviate from the expected behavior or the majority of the data. These deviations, known as outliers, can indicate errors in data collection, unusual events, or the presence of pr...")
01:08, 21 March 2023 Walle talk contribs created page Out-group homogeneity bias (Created page with "{{see also|Machine learning terms}} ==Out-group Homogeneity Bias== Out-group homogeneity bias, also known as the out-group homogeneity effect, refers to the cognitive bias that leads individuals to perceive members of an out-group, or those that do not belong to their own social or cultural group, as more similar to one another than they actually are. This bias can manifest in various social, cultural, and demographic contexts, including ethnicity, nationality, gender, a...")
01:08, 21 March 2023 Walle talk contribs created page Optimizer (Created page with "{{see also|Machine learning terms}} ==Definition== An '''optimizer''' in machine learning is an algorithm or method used to adjust the parameters of a model with the aim of minimizing the error or loss function during the training process. Optimizers guide the model in learning patterns from the data and making predictions as accurately as possible. They are a crucial component of machine learning algorithms, as they determine the effectiveness and efficiency of the...")