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05:04, 20 March 2023 Walle talk contribs created page Hyperplane (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''hyperplane''' refers to a geometric construct that serves as a decision boundary for separating different classes or categories of data points in a multidimensional space. It is an essential concept for many classification and regression algorithms, including the popular Support Vector Machines (SVM) method. Mathematically, a hyperplane is an (n-1)-dimensional subspace within an n-dimens...")
05:04, 20 March 2023 Walle talk contribs created page Holdout data (Created page with "{{see also|Machine learning terms}} ==Holdout Data in Machine Learning== Holdout data is a subset of the dataset in machine learning that is separated from the training data and is used to evaluate the performance of a model during the model selection process. Holdout data helps to identify potential issues such as overfitting and provides an unbiased estimate of the model's generalization performance. This section discusses the importance of holdout data, the pr...")
05:04, 20 March 2023 Walle talk contribs created page Hinge loss (Created page with "{{see also|Machine learning terms}} ==Hinge Loss== Hinge loss is a type of loss function used in machine learning and specifically in support vector machines (SVMs). It measures the error between the predicted output and the actual output for a given training example. Hinge loss is particularly effective for binary classification problems, as it aims to find the optimal decision boundary (or margin) that maximally separates two classes of data points. ===Definit...")
05:04, 20 March 2023 Walle talk contribs created page Heuristic (Created page with "{{see also|Machine learning terms}} ==Definition of Heuristic== Heuristics, derived from the Greek word ''heuriskein'' which means "to discover," are problem-solving techniques that employ a practical approach to finding an adequate, though not always optimal, solution to complex problems. In machine learning, heuristics are often utilized to guide the search for an appropriate model or to optimize algorithmic parameters when an exhaustive search is computationally i...")
05:03, 20 March 2023 Walle talk contribs created page Hashing (Created page with "{{see also|Machine learning terms}} ==Hashing in Machine Learning== Hashing, a technique commonly used in computer science, has found various applications in the field of machine learning. In this context, hashing mainly refers to the process of converting high-dimensional input data into lower-dimensional representations, while preserving important information about the original data. This transformation can be beneficial for numerous machine learning tasks, including f...")
05:03, 20 March 2023 Walle talk contribs created page Hallucination (Created page with "{{see also|Machine learning terms}} ==Hallucination in Machine Learning== Hallucination in machine learning refers to the phenomenon where a model generates outputs that are not entirely accurate or relevant to the input data. This occurs when the model overfits to the training data or does not generalize well to new or unseen data. This behavior has been observed in various machine learning models, including deep learning models like neural networks and natural lang...")
05:03, 20 March 2023 Walle talk contribs created page Group attribution bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Group attribution bias is a term used to describe a phenomenon in machine learning where an algorithm systematically and unfairly associates certain characteristics or outcomes with specific groups of individuals. This bias often stems from the training data that a machine learning model is exposed to, which may contain unrepresentative or skewed examples. When a model is trained on such data, it may inadvertently lear...")
05:03, 20 March 2023 Walle talk contribs created page Graph execution (Created page with "{{see also|Machine learning terms}} ==Graph Execution in Machine Learning== Graph execution in machine learning refers to a computational paradigm that employs directed graphs to represent and execute complex operations and dependencies between data, models, and algorithms. The graph execution approach is typically used in conjunction with TensorFlow, a popular open-source machine learning library, to optimize performance and parallelism in deep learning models. It p...")
05:03, 20 March 2023 Walle talk contribs created page Graph (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, a '''graph''' is a mathematical structure that represents relationships between objects or entities, typically in the form of nodes (or vertices) connected by edges (or links). Graphs have become increasingly popular in recent years due to their ability to represent complex data and their effectiveness in solving various machine learning tasks. They are particularly useful for repres...")
05:03, 20 March 2023 Walle talk contribs created page Layers API (tf.layers) (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Layers API''' (commonly referred to as '''tf.layers''') is a high-level interface within the TensorFlow machine learning framework, specifically designed to simplify the process of building and training neural networks. It provides pre-built, reusable components, known as layers, that can be easily combined and customized to create a wide range of machine learning models. The Layers API encourages modular de...")
05:02, 20 March 2023 Walle talk contribs created page Kernel Support Vector Machines (KSVMs) (Created page with "{{see also|Machine learning terms}} ==Introduction== Kernel Support Vector Machines (KSVMs) are a class of machine learning algorithms that are particularly well-suited for classification and regression tasks. They are an extension of the Support Vector Machine (SVM) algorithm and utilize kernel functions to project data into a higher-dimensional space, allowing for nonlinear decision boundaries. This article aims to provide an academic-style overview of the key...")
05:02, 20 March 2023 Walle talk contribs created page Keras (Created page with "{{see also|Machine learning terms}} ==Introduction== Keras is an open-source, high-level neural networks API (Application Programming Interface) designed to simplify the process of building and training deep learning models. It was developed by François Chollet and released in March 2015. Keras acts as a user-friendly interface to the more complex deep learning libraries like TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). By providin...")
05:02, 20 March 2023 Walle talk contribs created page IoU (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and computer vision, '''Intersection over Union (IoU)''' is a commonly used evaluation metric for object detection and segmentation tasks. This metric measures the overlap between the predicted bounding box (or segmentation mask) and the ground truth bounding box (or segmentation mask), providing a quantitative assessment of the model's performance in terms of its ability to acc...")
03:42, 20 March 2023 Alpha5 talk contribs created page GPT-4 (Created page with "==Exams== {| class="wikitable" ! Exam ! GPT-4 Estimated Percentile ! GPT-4 (no vision) Estimated Percentile ! GPT-3.5 Estimated Percentile |- | Uniform Bar Exam (MBE+MEE+MPT)1 | 298 / 400 (~90th) | 298 / 400 (~90th) | 213 / 400 (~10th) |- | LSAT | 163 (~88th) | 161 (~83rd) | 149 (~40th) |- | SAT Evidence-Based Reading & Writing | 710 / 800 (~93rd) | 710 / 800 (~93rd) | 670 / 800 (~87th) |- | SAT Math | 700 / 800 (~89th) | 690 / 800 (~89th)...")
01:19, 20 March 2023 Walle talk contribs created page Gradient (Created page with "{{see also|Machine learning terms}} ==Gradient in Machine Learning== Gradient is a fundamental concept in machine learning, particularly in the optimization process of training algorithms. It is a multi-dimensional generalization of the derivative, which quantifies the rate of change of a function with respect to its variables. In machine learning, the gradient is typically used to minimize a loss function, which measures the discrepancy between the predictions made by t...")
01:18, 20 March 2023 Walle talk contribs created page Generator (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''generator''' refers to a model or algorithm that generates new data samples, which can be either synthetic or based on existing data. Generators have become increasingly popular with the advent of generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are capable of generating complex data distributions. These models ha...")
01:18, 20 March 2023 Walle talk contribs created page Generative model (Created page with "{{see also|Machine learning terms}} ==Introduction== A generative model is a type of machine learning algorithm that aims to learn the underlying probability distribution of the training data in order to generate new data samples that resemble the original dataset. These models have been widely adopted in various applications such as natural language processing, image synthesis, and anomaly detection. ==Types of Generative Models== Generative models can be broadly c...")
01:18, 20 March 2023 Walle talk contribs created page Generative adversarial network (GAN) (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Generative Adversarial Network''' ('''GAN''') is a type of machine learning algorithm developed by Ian Goodfellow and his colleagues in 2014<ref>Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In ''Advances in neural information processing systems'' (pp. 2672-2680).</ref>. GANs are comprised of two distinct...")
01:18, 20 March 2023 Walle talk contribs created page Generalized linear model (Created page with "{{see also|Machine learning terms}} ==Generalized Linear Models (GLMs)== Generalized Linear Models (GLMs) are a class of statistical models that extend the linear regression model, allowing for response variables with distributions other than the normal distribution. GLMs were first introduced by John Nelder and Robert Wedderburn in 1972, and have since become a fundamental tool in statistical modeling and machine learning. ===Components of a Generalized Linear Mode...")
01:18, 20 March 2023 Walle talk contribs created page Fully connected layer (Created page with "{{see also|Machine learning terms}} ==Fully Connected Layer in Machine Learning== The fully connected layer, also known as a dense layer, is an essential component in various machine learning models, particularly deep learning architectures such as artificial neural networks (ANNs) and convolutional neural networks (CNNs). This layer serves to connect each neuron in one layer to every neuron in the subsequent layer, enabling information to be transmitted and proc...")
01:18, 20 March 2023 Walle talk contribs created page Full softmax (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the softmax function is an essential component for the classification of multiple categories. The full softmax, also known as the standard softmax, is a method used to convert a vector of real numbers into a probability distribution. The output of the full softmax function is a probability distribution that can be interpreted as the likelihood of an input belonging to each of the considered classes...")
01:17, 20 March 2023 Walle talk contribs created page Fine tuning (Created page with "{{see also|Machine learning terms}} ==Introduction== Fine tuning, also known as transfer learning, is a technique used in machine learning to improve the performance of a pre-trained model on a specific task. This approach leverages the knowledge gained from a related task, typically one with a larger dataset, to fine-tune the model for a new task with a smaller dataset. Fine tuning has gained popularity in deep learning, especially for tasks involving Convolutional Ne...")
01:17, 20 March 2023 Walle talk contribs created page Few-shot learning (Created page with "{{see also|Machine learning terms}} ==Few-shot Learning in Machine Learning== Few-shot learning is a subfield of machine learning, particularly focused on training algorithms to perform tasks or make predictions with a limited amount of data. In contrast to traditional machine learning, which often relies on large volumes of data for training, few-shot learning aims to achieve similar performance using only a few samples. ===Background and Motivation=== The development...")
01:17, 20 March 2023 Walle talk contribs created page Feedforward neural network (FFN) (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''feedforward neural network''' (FFN) is a type of artificial neural network used in machine learning that is characterized by its unidirectional flow of information, from input to output, without any loops or cycles. The network is composed of layers of interconnected nodes, known as neurons or artificial neurons, that process and transmit information. Feedforward neural networks have been used extensively in vario...")
01:17, 20 March 2023 Walle talk contribs created page Federated learning (Created page with "{{see also|Machine learning terms}} ==Introduction== Federated learning is a decentralized approach to machine learning that aims to enable multiple participants to collaboratively train a shared model while keeping their data private. This method has garnered significant attention in recent years due to its potential to address privacy, security, and scalability concerns in distributed machine learning systems. The core principle of federated learning is to allow local...")
01:17, 20 March 2023 Walle talk contribs created page Feature spec (Created page with "{{see also|Machine learning terms}} ==Feature Specification in Machine Learning== Feature specification is a crucial aspect of machine learning and data preprocessing that involves defining and selecting the relevant features or attributes for a given problem. The process is essential to improve model performance, reduce computational complexity, and facilitate easier interpretation of the results. ===Definition=== In machine learning, features refer to the meas...")
01:17, 20 March 2023 Walle talk contribs created page Feature extraction (Created page with "{{see also|Machine learning terms}} ==Introduction== Feature extraction is a crucial step in the field of machine learning and pattern recognition that involves extracting relevant and informative attributes from raw data. These attributes, also known as features or variables, are then used by machine learning algorithms to classify or predict outcomes. The process of feature extraction is essential in simplifying and enhancing the performance of models by reduci...")
01:16, 20 March 2023 Walle talk contribs created page False negative rate (Created page with "{{see also|Machine learning terms}} ==Definition== The '''false negative rate''' (Type II error) in machine learning refers to the proportion of positive instances that the algorithm incorrectly classifies as negative. This is an important metric when evaluating the performance of machine learning models, particularly when assessing the capability of the model to accurately identify positive cases. The false negative rate is complementary to the sensitivity (re...")
01:16, 20 March 2023 Walle talk contribs created page Fairness metric (Created page with "{{see also|Machine learning terms}} ==Fairness Metric in Machine Learning== In the field of machine learning, fairness is an increasingly important consideration. The concept of fairness relates to the equitable treatment of different groups by algorithms and the avoidance of discriminatory outcomes. Fairness metrics are quantitative measures that help assess the fairness of a machine learning model, thus allowing researchers and practitioners to mitigate potential biase...")
01:16, 20 March 2023 Walle talk contribs created page Fairness constraint (Created page with "{{see also|Machine learning terms}} ==Fairness Constraint in Machine Learning== Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms that enable computers to learn from and make predictions or decisions based on data. In the context of machine learning, fairness is an important ethical consideration, as it pertains to the equitable treatment of different individuals or groups by the algorithm. Fairness constraint...")
01:16, 20 March 2023 Walle talk contribs created page Experimenter's bias (Created page with "{{see also|Machine learning terms}} ==Experimenter's Bias in Machine Learning== Experimenter's bias, also known as researcher bias or confirmation bias, is a phenomenon that occurs when researchers unintentionally influence the outcomes of their studies or experiments to align with their expectations or preconceived beliefs. In the context of machine learning, experimenter's bias can manifest in various stages of the development process, including data collection, prepro...")
01:16, 20 March 2023 Walle talk contribs created page Equalized odds (Created page with "{{see also|Machine learning terms}} ==Equalized Odds in Machine Learning== Equalized Odds is a fairness criterion in machine learning, which aims to mitigate discriminatory outcomes that may arise from the use of algorithms in various applications. This criterion focuses on ensuring that the error rates for different demographic groups are equal, in order to avoid biased decision-making. In the following sections, we will delve into the definition, motivation, and implem...")
01:16, 20 March 2023 Walle talk contribs created page Equality of opportunity (Created page with "{{see also|Machine learning terms}} ==Equality of Opportunity in Machine Learning== Equality of opportunity in machine learning refers to the design, implementation, and assessment of algorithms and models that ensure fairness and unbiased outcomes for different subgroups within a given population. This is particularly important when these models are used to make decisions that may have significant impacts on people's lives, such as job applications, loan approvals, or m...")
01:16, 20 March 2023 Walle talk contribs created page Ensemble (Created page with "{{see also|Machine learning terms}} ==Ensemble Methods in Machine Learning== Ensemble methods are a group of techniques in machine learning that combine the predictions of multiple models, or "base learners," to improve overall predictive performance. The idea behind ensemble methods is that the aggregation of the predictions of several individual models can lead to a more robust and accurate result than any single model alone. ===Types of Ensemble Methods=== There...")
01:15, 20 March 2023 Walle talk contribs created page Empirical risk minimization (ERM) (Created page with "{{see also|Machine learning terms}} ==Empirical Risk Minimization (ERM)== Empirical Risk Minimization (ERM) is a fundamental concept in the field of machine learning and statistical learning theory. ERM is a strategy that aims to minimize the risk of making incorrect predictions by selecting the best hypothesis from a given hypothesis set. The risk is defined as the expected loss incurred when using the selected hypothesis to make predictions on unseen data. ERM...")
01:15, 20 March 2023 Walle talk contribs created page Earth mover's distance (EMD) (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Earth Mover's Distance''' (EMD), also known as the '''Wasserstein distance''' or '''Mallows distance''', is a measure of dissimilarity between two probability distributions in machine learning, statistics, and computer vision. It was originally introduced by Y. Rubner, C. Tomasi, and L.J. Guibas in their 1998 paper titled "A Metric for Distributions with Applications to Image Databases". EMD is especially useful...")
01:15, 20 March 2023 Walle talk contribs created page GAN (Created page with "{{see also|Machine learning terms}} ==Generative Adversarial Networks (GANs)== Generative Adversarial Networks, or GANs, are a class of machine learning models introduced by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a process of competing against each other. GANs have been widely used in various applications, including image synthesis, data augmentation, and sem...")
01:15, 20 March 2023 Walle talk contribs created page Estimator (Created page with "{{see also|Machine learning terms}} ==Estimator in Machine Learning== In the context of machine learning, an '''estimator''' is an algorithm or function that approximates a target function or model based on a set of input data. The primary goal of an estimator is to make predictions or infer properties of an unknown function using observed data. Estimators can be broadly categorized into two types: '''parametric''' and '''non-parametric'''. ==Parametric Estimators== Par...")
19:17, 19 March 2023 Walle talk contribs created page Eager execution (Created page with "{{see also|Machine learning terms}} ==Introduction== Eager execution is a programming paradigm in machine learning that offers a more intuitive and flexible way of building, training, and debugging computational graphs. Unlike the traditional graph-based execution, which requires the construction of a static computation graph before running any operations, eager execution allows operations to be executed immediately as they are called, similar to standard Python programs...")
19:17, 19 March 2023 Walle talk contribs created page Dropout regularization (Created page with "{{see also|Machine learning terms}} ==Dropout Regularization in Machine Learning== Dropout regularization is a technique used in machine learning to prevent overfitting in neural networks. Overfitting occurs when a model learns to perform well on the training data but fails to generalize to unseen data. This article discusses the concept of dropout regularization, its implementation, and its advantages in the context of neural networks. ===Concept=== Dropout regularizat...")
19:17, 19 March 2023 Walle talk contribs created page Disparate treatment (Created page with "{{see also|Machine learning terms}} ==Disparate Treatment in Machine Learning== Disparate treatment in machine learning refers to the unjust or prejudicial treatment of individuals or groups based on certain attributes, such as race, gender, or age, in the context of algorithmic decision-making systems. This phenomenon occurs when the model learns to make biased decisions due to the presence of discriminatory patterns in the training data, resulting in unfair treatment f...")
19:17, 19 March 2023 Walle talk contribs created page Disparate impact (Created page with "{{see also|Machine learning terms}} ==Disparate Impact in Machine Learning== Disparate impact in machine learning refers to the unintended and potentially discriminatory consequences of an algorithmic decision-making process, where certain groups or individuals may be adversely affected due to biases in the data or model. This phenomenon raises significant ethical, legal, and social concerns, as it may perpetuate or exacerbate existing inequalities. ===Causes of Dispara...")
19:16, 19 March 2023 Walle talk contribs created page Discriminator (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''discriminator''' in the context of machine learning refers to a model or a component of a model designed to distinguish between different types of data. Discriminators are most commonly used in Generative Adversarial Networks (GANs), where they play a crucial role in the training process by evaluating the authenticity of generated data samples. This article provides an overview of discriminators, their applica...")
19:16, 19 March 2023 Walle talk contribs created page Discriminative model (Created page with "{{see also|Machine learning terms}} ==Discriminative Models in Machine Learning== Discriminative models are a class of machine learning algorithms that aim to model the decision boundary between different classes or categories. These models focus on estimating the conditional probability of a class label given a set of input features, denoted as P(Y|X), where Y represents the class label and X the input features. Discriminative models are widely used for various tasks, s...")
19:16, 19 March 2023 Walle talk contribs created page Dimensions (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, dimensions refer to the number of features or attributes used to represent data points in a dataset. High-dimensional data can pose challenges to traditional machine learning algorithms, while also providing opportunities for more complex and detailed analyses. This article will explore the concept of dimensions in machine learning, their implications, and strategies for dealing wi...")
19:16, 19 March 2023 Walle talk contribs created page Dimension reduction (Created page with "{{see also|Machine learning terms}} ==Dimension Reduction in Machine Learning== Dimension reduction, also known as dimensionality reduction, is a fundamental technique in the field of machine learning and data analysis. The primary goal of dimension reduction is to reduce the number of features or variables in a dataset while preserving its underlying structure and information. This process aids in improving computational efficiency, reducing the risk of overfitt...")
19:16, 19 March 2023 Walle talk contribs created page Device (Created page with "{{see also|Machine learning terms}} ==Device in Machine Learning== The term "device" in the context of machine learning generally refers to the hardware that is utilized for running machine learning algorithms, models, and training processes. Devices can range from basic personal computers to powerful, specialized processors designed specifically for machine learning tasks. In this article, we will explore the various types of devices used in machine learning, their char...")
19:16, 19 March 2023 Walle talk contribs created page Derived label (Created page with "{{see also|Machine learning terms}} ==Derived Label in Machine Learning== In machine learning, a '''derived label''' refers to the output variable that has been transformed or computed from the raw data in order to improve the performance or interpretability of a model. The process of creating derived labels often involves feature engineering and domain expertise to determine the most relevant or meaningful representations of the data. ===Feature Engineering and Derived...")
19:15, 19 March 2023 Walle talk contribs created page Dense layer (Created page with "{{see also|Machine learning terms}} ==Dense Layer in Machine Learning== A '''dense layer''' in machine learning, also referred to as a '''fully connected layer''' or simply '''FC layer''', is a fundamental architectural component of artificial neural networks (ANNs) and deep learning models. The dense layer functions as a linear transformation followed by an optional non-linear activation function, which facilitates the learning and representation of complex...")
19:15, 19 March 2023 Walle talk contribs created page Demographic parity (Created page with "{{see also|Machine learning terms}} ==Demographic Parity in Machine Learning== Demographic parity, also known as statistical parity, is a fairness metric used in machine learning to assess the performance of classification algorithms with respect to different demographic groups. It measures the extent to which an algorithm's predictions are unbiased with respect to a protected attribute, such as gender, race, or age. The goal of demographic parity is to ensure equal trea...")