User contributions for Walle

18 March 2023

• 13:2313:23, 18 March 2023 diff hist +3,011‎ N Negative class ‎ Created page with "{{see also|Machine learning terms}} ==Negative Class in Machine Learning== The negative class in machine learning refers to the category or label assigned to instances in a dataset that do not possess the characteristics or features of interest. It is the counterpart to the positive class, which represents instances with the desired attributes. The concept of negative and positive classes is particularly relevant in binary classification problems, where the goal..." current
• 13:2313:23, 18 March 2023 diff hist +4,198‎ N Natural language understanding ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Natural Language Understanding (NLU) is a subfield of Artificial Intelligence and Computational Linguistics, concerned with enabling machines to comprehend, interpret, and generate human language in a meaningful way. NLU plays a pivotal role in the development of Machine Learning models, which are designed to automatically learn and improve from experience, with a focus on tasks such as Sentiment Analysis..." current
• 13:2313:23, 18 March 2023 diff hist +4,018‎ N Multimodal model ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== A '''multimodal model''' in machine learning is an advanced computational approach that involves the integration and processing of multiple types of data, or modalities, to enhance the learning process and improve predictive performance. Multimodal models aim to capture complex patterns and relationships that exist within and across various data modalities, such as text, images, audio, and video, to enable more acc..." current
• 13:2313:23, 18 March 2023 diff hist +3,303‎ N Multi-head self-attention ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Multi-head self-attention is a core component of the Transformer architecture, a type of neural network introduced by Vaswani et al. (2017) in the paper "Attention Is All You Need". This mechanism allows the model to capture complex relationships between the input tokens by weighing their importance with respect to each other. The multi-head aspect refers to the parallel attention computations performed on differen..." current
• 13:2313:23, 18 March 2023 diff hist +4,009‎ N Multi-class classification ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Multi-class classification is a type of supervised learning problem in machine learning where an algorithm is tasked with categorizing instances into one of multiple possible classes. In contrast to binary classification, which deals with only two classes, multi-class classification handles three or more classes. This article provides an overview of multi-class classification, discusses common techniques an..." current
• 13:2313:23, 18 March 2023 diff hist +4,375‎ N Model parallelism ‎ Created page with "{{see also|Machine learning terms}} ==Model Parallelism in Machine Learning== Model parallelism is an approach in machine learning that addresses the computational challenges posed by the increasing size and complexity of modern neural network models. It involves the concurrent execution of different parts of a single model across multiple processing units, often in parallel to other parts of the model. This article discusses the motivation behind model parallelism,..." current
• 13:2213:22, 18 March 2023 diff hist +3,122‎ N Model ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''model''' refers to a mathematical representation or abstraction of a real-world process or phenomenon. Machine learning models are developed using algorithms that learn from and make predictions or decisions based on input data. The primary goal of these models is to generalize from the training data in order to accurately predict outcomes for unseen data points. ==Types of M..." current
• 13:2213:22, 18 March 2023 diff hist +3,995‎ N Modality ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, '''modality''' refers to the different types, forms, or structures of data that a model can process or learn from. Understanding the concept of modality is essential for designing and implementing machine learning algorithms that can handle diverse data types effectively. This article discusses the concept of modality in machine learning, its various types, and its importance in mode..." current
• 13:2113:21, 18 March 2023 diff hist +4,259‎ N Meta-learning ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Meta-learning, also referred to as "learning to learn", is an advanced paradigm in the field of machine learning that focuses on the design of algorithms and models capable of improving their performance on new tasks by utilizing previous learning experiences. The primary objective of meta-learning is to develop models that can adapt quickly to new tasks with minimal data and training time. This article provides an..." current
• 13:2113:21, 18 March 2023 diff hist +2,712‎ N Masked language model ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, particularly natural language processing (NLP), a '''masked language model''' (MLM) is an important and widely used approach to train deep learning models on large-scale text data. This unsupervised technique has gained significant attention due to its success in various NLP tasks, such as text classification, translation, and sentiment analysis. ==Masked Language Modeling== Masked la..." current
• 13:1913:19, 18 March 2023 diff hist +2,990‎ N Loss function ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''loss function''' (also known as a cost function or objective function) is a crucial mathematical formulation that quantifies the difference between the predicted outcome of a model and the actual or desired outcome. Loss functions serve as the basis for optimization, enabling the model to iteratively adjust its parameters to minimize this difference and improve its performance..." current
• 13:1913:19, 18 March 2023 diff hist +2,890‎ N Loss curve ‎ Created page with "{{see also|Machine learning terms}} ==Loss Curve in Machine Learning== In the field of machine learning, a '''loss curve''' is a graphical representation that demonstrates the performance of a learning algorithm during its training process. By plotting the value of the loss function against the number of training iterations or epochs, researchers and practitioners can assess the algorithm's progress in learning the underlying patterns in the given dataset. ===Im..." current
• 13:1913:19, 18 March 2023 diff hist +3,549‎ N Loss ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, ''loss'' refers to a quantitative measure of the discrepancy between a model's predicted outputs and the true or observed values. It serves as an evaluation metric to assess the performance of a machine learning algorithm during the training process. By minimizing the loss function, practitioners aim to improve the model's accuracy and generalization capabilities. ==Loss Functions..." current
• 13:1913:19, 18 March 2023 diff hist +2,793‎ N Logistic regression ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. In the context of machine learning, logistic regression is a supervised learning algorithm used for solving binary classification problems. It predicts the probability of an event occurring based on the given input features. ==Logistic Regression Model== ===Model F..." current
• 13:1913:19, 18 March 2023 diff hist +3,328‎ N Log-odds ‎ Created page with "{{see also|Machine learning terms}} ==Log-odds in Machine Learning== Log-odds, also known as logit, is a concept frequently used in machine learning, particularly in the context of binary classification problems. It is a method of representing the probability of an event occurring in the form of a logarithmic function. The log-odds function is often used in conjunction with logistic regression, a popular machine learning algorithm for classification tasks. ===Definition..." current
• 13:1913:19, 18 March 2023 diff hist +2,863‎ N Linear regression ‎ Created page with "{{see also|Machine learning terms}} ==Linear Regression in Machine Learning== Linear regression is a fundamental supervised learning technique used in machine learning and statistics to model the relationship between a dependent variable and one or more independent variables. It is a linear approach that assumes a linear relationship between input and output variables. ===Overview=== In machine learning, linear regression is a popular algorithm for solving reg..." current
• 13:1913:19, 18 March 2023 diff hist +3,597‎ N Linear model ‎ Created page with "{{see also|Machine learning terms}} ==Linear Models in Machine Learning== Linear models are a class of statistical models and machine learning algorithms that assume a linear relationship between input features and output. They are often used for regression and classification tasks due to their simplicity and ease of interpretation. ===Introduction=== In machine learning, linear models are used to predict a target variable based on one or more input features. These..." current
• 13:1813:18, 18 March 2023 diff hist +3,518‎ N Linear ‎ Created page with "{{see also|Machine learning terms}} ==Linear Models in Machine Learning== ===Introduction=== In machine learning, linear models are a class of algorithms that utilize a linear relationship between input features and the output variable to make predictions. These models assume that the relationship between the input features (independent variables) and the output (dependent variable) can be represented by a straight line, or more generally, a hyperplane in higher-..." current
• 13:1613:16, 18 March 2023 diff hist +3,821‎ N Large language model ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== A large language model in machine learning refers to an advanced type of artificial intelligence that is designed to understand and generate human-like text. These models are trained on vast amounts of text data and can perform various tasks, such as translation, summarization, and question answering. The development of large language models has been driven by advancements in both deep learning and natural la..." current
• 13:1613:16, 18 March 2023 diff hist +3,427‎ N Language model ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== A '''language model''' in the context of machine learning is a computational model designed to understand and generate human language. Language models leverage statistical and probabilistic techniques to analyze, process, and produce text or speech data, making them indispensable in a wide range of natural language processing (NLP) tasks. Over time, the development of increasingly sophisticated models has led to signif..."
• 13:1513:15, 18 March 2023 diff hist +2,526‎ N Lambda ‎ Created page with "{{see also|Machine learning terms}} ==Lambda in Machine Learning== Lambda is a term commonly used in machine learning and refers to a hyperparameter associated with regularization techniques. It is particularly relevant in the context of linear regression and logistic regression models, where regularization is employed to prevent overfitting and improve the generalization ability of the model. The two most popular regularization techniques using lambda are L1 reg..." current
• 13:1513:15, 18 March 2023 diff hist +3,682‎ N Labeled example ‎ Created page with "{{see also|Machine learning terms}} ==Labeled Example in Machine Learning== ===Definition=== In the field of machine learning, a labeled example refers to a data point that consists of an input feature vector and its corresponding output value, often referred to as the target or label. Labeled examples are essential for supervised learning algorithms, which use these examples to learn a model that can make predictions or classifications on unseen data. The process of..." current
• 13:1513:15, 18 March 2023 diff hist +3,008‎ N Label ‎ Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''label''' refers to the desired output, or the "correct" value, for a particular instance in a dataset. Labels are used in supervised learning algorithms, where the goal is to learn a mapping from input data to output data, based on a set of examples containing input-output pairs. These output values in the training dataset are known as labels. The process of assigning labels to instances..." current
• 13:1513:15, 18 March 2023 diff hist +3,546‎ N Encoder ‎ Created page with "{{see also|Machine learning terms}} ==Overview== An '''encoder''' in the context of machine learning refers to a specific component of a broader class of algorithms, typically used in unsupervised learning tasks, such as dimensionality reduction and representation learning. Encoders work by transforming input data into a lower-dimensional, more compact representation, which can be efficiently used for further processing, such as for clustering, classificati..." current
• 13:1513:15, 18 March 2023 diff hist +3,376‎ N Embedding vector ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== An '''embedding vector''' in machine learning refers to a continuous, dense representation of discrete objects such as words, images, or nodes in a graph. Embedding vectors are used to convert these discrete objects into a continuous space, which makes it easier to apply machine learning algorithms that rely on mathematical operations. Typically, these embeddings are generated through unsupervised or supervised learnin..." current
• 13:1513:15, 18 March 2023 diff hist +3,971‎ N Embedding space ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the concept of '''embedding space''' refers to a continuous, high-dimensional space where objects, such as words, images, or user profiles, can be represented as vectors. These vector representations capture the underlying relationships and semantics of the objects in a more compact and computationally efficient manner. Embedding spaces are utilized in various machine learning applications, includi..." current
• 13:1413:14, 18 March 2023 diff hist +4,226‎ N Denoising ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, denoising refers to the process of removing noise from the input data, which can significantly improve the performance and reliability of the resulting models. Noise in data can arise from various sources, such as measurement errors, transmission errors, or other disturbances. Denoising techniques play a crucial role in many applications, including image processing, speech recognition,..." current
• 13:1413:14, 18 March 2023 diff hist +2,808‎ N Decoder ‎ Created page with "{{see also|Machine learning terms}} ==Decoder in Machine Learning== The '''decoder''' is a fundamental component in various machine learning architectures, particularly in sequence-to-sequence (seq2seq) models and autoencoders. It is responsible for generating output sequences or reconstructing input data based on the internal representation or context vector provided by the encoder. Decoders can be utilized in a wide array of applications such as natural langu..." current
• 13:1413:14, 18 March 2023 diff hist +3,240‎ N Crash blossom ‎ Created page with "{{see also|Machine learning terms}} ==Crash Blossom in Machine Learning== Crash blossom is a term that originates from the field of journalism and linguistic ambiguity, referring to a headline that can be interpreted in more than one way, often resulting in humorous or confusing interpretations. However, in the context of machine learning, crash blossom does not have a direct application or meaning. Nevertheless, we can discuss related concepts in machine learning that t..." current
• 13:1413:14, 18 March 2023 diff hist +3,395‎ N Confusion matrix ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and pattern recognition, a '''confusion matrix''', also known as an '''error matrix''' or '''classification matrix''', is a specific table layout that allows for visualization and analysis of the performance of an algorithm, usually a classifier. It is a useful tool to assess the correctness and accuracy of a classification model by comparing the predicted outcomes with the actu..." current
• 13:1413:14, 18 March 2023 diff hist +3,652‎ N Causal language model ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== A '''causal language model''' is a type of machine learning model designed to generate text by predicting the next word in a sequence based on the context of the preceding words. These models are particularly useful in natural language processing (NLP) tasks, as they can capture the inherent structure and meaning of language in a probabilistic manner. Causal language models, which are also known as autoregressive l..." current
• 13:1413:14, 18 March 2023 diff hist +2,718‎ N Bigram ‎ Created page with "{{see also|Machine learning terms}} ==Bigram in Machine Learning== A '''bigram''' is a fundamental concept in the field of natural language processing (NLP), a subfield of machine learning. Bigrams are pairs of consecutive words in a given text or sequence of words. They play a vital role in various NLP tasks, such as language modeling, text classification, and sentiment analysis, by capturing the contextual information of words in a language. ===Definition and..."
• 13:1413:14, 18 March 2023 diff hist +3,105‎ N Bidirectional language model ‎ Created page with "{{see also|Machine learning terms}} ==Bidirectional Language Models in Machine Learning== Bidirectional language models (BiLMs) are a type of machine learning model that are specifically designed for natural language processing (NLP) tasks. They have gained popularity in recent years due to their superior ability to understand and generate human-like text. This article provides an overview of bidirectional language models, their architecture, and applications in NLP task..." current
• 13:1313:13, 18 March 2023 diff hist +3,284‎ N Bidirectional ‎ Created page with "{{see also|Machine learning terms}} ==Bidirectional Approaches in Machine Learning== Bidirectional approaches in machine learning refer to a class of algorithms designed to process and analyze data sequences in both forward and backward directions. These algorithms are particularly useful for tasks involving natural language processing, time series analysis, and other domains where temporal or sequential dependencies exist within the data. In this article, we will discus..." current
• 13:1313:13, 18 March 2023 diff hist +3,334‎ N Bag of words ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the '''bag of words''' (BoW) model is a common and simplified representation method used for natural language processing (NLP) and text classification tasks. The primary goal of the BoW model is to convert a collection of text documents into numerical feature vectors, which can be used as input for machine learning algorithms. ==Methodology== The bag of words model comprises two main..." current
• 13:1313:13, 18 March 2023 diff hist +2,635‎ N Root Mean Squared Error (RMSE) ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, '''Root Mean Squared Error (RMSE)''' is a widely used metric for evaluating the performance of regression models. It quantifies the difference between the predicted values and the true values by calculating the square root of the average of the squared differences. The RMSE is particularly useful because it gives a measure of error that is interpretable in the same unit as the original..." current
• 13:1313:13, 18 March 2023 diff hist +2,677‎ N Rectified Linear Unit (ReLU) ‎ Created page with "{{see also|Machine learning terms}} ==Rectified Linear Unit (ReLU)== The Rectified Linear Unit (ReLU) is a widely-used activation function in the field of machine learning and deep learning. It is a non-linear function that helps to model complex patterns and relationships in data. ReLU has gained significant popularity because of its simplicity and efficiency in training deep neural networks. ===History of ReLU=== The concept of ReLU can be traced back to t..." current
• 13:1313:13, 18 March 2023 diff hist +2,929‎ N ReLU ‎ Created page with "{{see also|Machine learning terms}} ==ReLU in Machine Learning== ReLU, or '''Rectified Linear Unit''', is a popular activation function used in artificial neural networks (ANNs) for implementing deep learning models. The primary role of an activation function is to introduce non-linearity in the model and improve its learning capability. ReLU has been widely adopted due to its simplicity, efficiency, and ability to mitigate the vanishing gradient problem...." current
• 13:1313:13, 18 March 2023 diff hist +3,770‎ N ROC (receiver operating characteristic) Curve ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Receiver Operating Characteristic''' ('''ROC''') curve is a graphical representation that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is widely used in machine learning, statistics, and data analysis for evaluating the performance of classification algorithms, particularly in the presence of imbalanced class distribution. ==Background== ===Origi..." current
• 13:1213:12, 18 March 2023 diff hist +4,269‎ N NLU ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Natural Language Understanding (NLU) is a subfield of Artificial Intelligence (AI) and Machine Learning (ML) that focuses on enabling computers to comprehend and interpret human language. This process includes the analysis of linguistic data to identify key elements such as entities, relations, and sentiments. NLU enables machines to understand the meaning and context of natural language input, allowing them to..." current
• 13:1213:12, 18 March 2023 diff hist +3,105‎ N N-gram ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and natural language processing, an '''N-gram''' is a contiguous sequence of N items from a given sample of text or speech. N-grams are widely used for various tasks in computational linguistics, such as statistical language modeling, text classification, and information retrieval. The term "N-gram" is derived from the combination of the letter "N" and the word "gram," which originates..."
• 13:1213:12, 18 March 2023 diff hist +2,368‎ N Log Loss ‎ Created page with "{{see also|Machine learning terms}} ==Log Loss== Log Loss, also known as logarithmic loss or cross-entropy loss, is a common loss function used in machine learning for classification problems. It is a measure of the difference between the predicted probabilities and the true labels of a dataset. The Log Loss function quantifies the performance of a classifier by penalizing the predicted probabilities that deviate from the actual class labels. ==Usage in Machine Learning..." current
• 13:1213:12, 18 March 2023 diff hist +3,450‎ N LaMDA (Language Model for Dialogue Applications) ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== '''LaMDA''' ('''L'''anguage '''M'''odel for '''D'''ialogue '''A'''pplications) is a conversational AI model developed by Google in the field of machine learning. LaMDA aims to improve the interaction between humans and computers by enabling open-domain conversations, thereby allowing machines to understand and respond to a wide range of topics. This article discusses the design, functionality, and key aspects of La..." current
• 13:1213:12, 18 March 2023 diff hist +3,193‎ N L2 regularization ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== L2 regularization, also known as ridge regression or Tikhonov regularization, is a technique employed in machine learning to prevent overfitting and improve the generalization of a model. It is a form of regularization that adds a penalty term to the objective function, which helps in constraining the model's complexity. L2 regularization is particularly useful for linear regression models, but can also be appl..." current
• 13:1213:12, 18 March 2023 diff hist +2,352‎ N L2 loss ‎ Created page with "{{see also|Machine learning terms}} ==L2 Loss in Machine Learning== L2 Loss, also known as Euclidean Loss or Squared Error Loss, is a widely-used loss function in machine learning and deep learning. It is a popular choice for regression tasks, where the goal is to predict a continuous output value. L2 Loss quantifies the difference between the predicted output and the true output, providing a measure of model accuracy. ===Definition and Properties=== The L2 Loss is def..." current
• 13:1113:11, 18 March 2023 diff hist +3,011‎ N L1 regularization ‎ Created page with "{{see also|Machine learning terms}} ==L1 Regularization in Machine Learning== L1 regularization, also known as Lasso regularization or L1 norm, is a widely used regularization technique in machine learning and statistical modeling to prevent overfitting and enhance the generalization of the model. It achieves this by introducing a penalty term in the optimization objective that encourages sparsity in the model parameters. ===Overview=== Regularization techniques are emp..." current
• 13:1113:11, 18 March 2023 diff hist +3,112‎ N L1 loss ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, various loss functions are used to measure the discrepancy between predicted values and actual values. L1 loss, also known as ''Least Absolute Deviations'' (LAD) or ''Least Absolute Errors'' (LAE), is one such loss function used in regression problems to estimate model parameters. L1 loss calculates the sum of absolute differences between predicted and actual values, making it robust to outliers an..." current
• 13:1113:11, 18 March 2023 diff hist +3,859‎ N GPT (Generative Pre-trained Transformer) ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Generative Pre-trained Transformer''' ('''GPT''') is a series of machine learning models developed by OpenAI for natural language processing tasks. These models are based on the Transformer architecture introduced by Vaswani et al. in 2017. GPT models are designed to generate text by predicting subsequent words in a sequence, and have been applied to tasks such as text generation, translation, summarization,..." current
• 13:1113:11, 18 March 2023 diff hist +3,877‎ N BLEU (Bilingual Evaluation Understudy) ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Bilingual Evaluation Understudy''' ('''BLEU''') is an automatic evaluation metric used in the field of Natural Language Processing (NLP) to measure the quality of machine-generated translations. Developed by IBM Research in 2002, it compares translations generated by a machine with a set of human-generated reference translations. BLEU scores are widely used in the evaluation of machine translation system..." current
• 13:1113:11, 18 March 2023 diff hist +3,640‎ N BERT (Bidirectional Encoder Representations from Transformers) ‎ Created page with "{{see also|Machine learning terms}} ==Introduction== BERT, or '''Bidirectional Encoder Representations from Transformers''', is a pre-training technique for natural language understanding tasks in the field of machine learning. Developed by researchers at Google AI Language, BERT has significantly advanced the state of the art in a wide range of tasks, such as question answering, sentiment analysis, and named entity recognition. BERT's breakthrough lies in its abilit..." current