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..."