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13:28, 18 March 2023 Walle talk contribs created page Sparse representation (Created page with "{{see also|Machine learning terms}} ==Sparse Representation in Machine Learning== Sparse representation is a concept in machine learning and signal processing that involves encoding data or signals using a small number of non-zero coefficients. This approach has become popular due to its ability to capture the essential features of the data, while reducing the computational complexity and storage requirements. Sparse representations have been successfully applied in vari...")
13:28, 18 March 2023 Walle talk contribs created page Sparse feature (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a sparse feature is a representation of data that consists predominantly of zero or null values, indicating the absence of some attributes or characteristics. Sparse features can be found in various data types and domains, such as text data, image data, and graph data. Utilizing sparse features effectively can significantly improve the efficiency and performance of machine learning alg...")
13:28, 18 March 2023 Walle talk contribs created page Softmax (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the '''softmax function''' is a widely used mathematical function for transforming a vector of numerical values into a probability distribution. Softmax is particularly useful in classification tasks where the goal is to assign an input to one of several possible categories. Softmax is often employed in combination with neural networks, such as multilayer perceptrons and convolutional neu...")
13:28, 18 March 2023 Walle talk contribs created page Sigmoid function (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the '''sigmoid function''' is a widely used mathematical function that transforms input values into probabilities, ranging from 0 to 1. It is often employed in various types of machine learning algorithms, particularly in artificial neural networks and logistic regression models, to map continuous inputs to probabilities for binary classification tasks. The sigmoid function is characterized...")
13:28, 18 March 2023 Walle talk contribs created page Sequence-to-sequence task (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, particularly deep learning, a '''sequence-to-sequence (seq2seq) task''' refers to the process of mapping an input sequence to an output sequence. This type of task is particularly useful in various natural language processing (NLP) and time series prediction applications. It has gained significant attention in recent years due to the advancements in recurrent neural networks (RNNs)...")
13:27, 18 March 2023 Walle talk contribs created page Sentiment analysis (Created page with "{{see also|Machine learning terms}} ==Introduction== Sentiment analysis, also known as opinion mining or emotion AI, is a subfield of Natural Language Processing (NLP) in machine learning that focuses on determining the sentiment, emotions, or opinions expressed in a given text. It is commonly applied to a wide range of areas, such as social media monitoring, customer feedback analysis, and market research. ==Approaches to Sentiment Analysis== There are three pr...")
13:27, 18 March 2023 Walle talk contribs created page Self-attention (also called self-attention layer) (Created page with "{{see also|Machine learning terms}} ==Introduction== Self-attention, also known as the self-attention layer, is a mechanism used in machine learning models, particularly in deep learning architectures such as Transformers. It enables the models to weigh and prioritize different input elements based on their relationships and relevance to one another. Self-attention has been widely adopted in various applications, including nat...")
13:27, 18 March 2023 Walle talk contribs created page Regularization rate (Created page with "{{see also|Machine learning terms}} ==Regularization Rate in Machine Learning== Regularization is an important technique in machine learning that helps prevent overfitting, a common problem where a model performs well on the training data but does not generalize well to new, unseen data. The regularization rate, also known as the regularization parameter or hyperparameter, is a constant value used to control the strength of regularization applied to a learning algorithm....")
13:27, 18 March 2023 Walle talk contribs created page Regularization (Created page with "{{see also|Machine learning terms}} ==Regularization in Machine Learning== Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model learns to perform well on the training data but does not generalize well to unseen data. Regularization works by adding a penalty term to the objective function, which encourages the model to select simpler solutions that are more likely to generalize to new data. There are several types of reg...")
13:27, 18 March 2023 Walle talk contribs created page Regression model (Created page with "{{see also|Machine learning terms}} ==Introduction== A regression model in machine learning is a type of supervised learning algorithm that is designed to predict continuous output values, based on input features. The main goal of regression models is to understand the relationships between the dependent variable (target) and the independent variables (features). Regression models have been widely adopted in various fields such as finance, healthcare, and economics,...")
13:27, 18 March 2023 Walle talk contribs created page Rater (Created page with "{{see also|Machine learning terms}} ==Rater in Machine Learning== In the field of machine learning, a '''rater''' refers to an individual or group responsible for evaluating and scoring a model's predictions, usually by comparing them to a known ground truth. Raters play a crucial role in the development, training, and validation of machine learning algorithms, ensuring that models are accurate, reliable, and unbiased. ===Role of Raters in Machine Learning=== Raters are...")
13:26, 18 March 2023 Walle talk contribs created page Proxy labels (Created page with "{{see also|Machine learning terms}} ==Proxy Labels in Machine Learning== Proxy labels are a technique used in the field of machine learning to approximate the true labels of a dataset when obtaining the exact labels is infeasible or expensive. This method is particularly useful in situations where acquiring ground truth labels would require a significant investment of time or resources, or when the true labels are not directly observable. ===Applications=== Proxy la...")
13:26, 18 March 2023 Walle talk contribs created page Prediction (Created page with "{{see also|Machine learning terms}} ==Introduction== Prediction in machine learning refers to the process by which a trained model estimates or forecasts the outcome of a given input based on its learned patterns and relationships from past data. The prediction task is essential to various machine learning applications, including classification, regression, and time series forecasting. This article provides an overview of the concept of prediction in machine...")
13:26, 18 March 2023 Walle talk contribs created page Post-processing (Created page with "{{see also|Machine learning terms}} ==Introduction== Post-processing, in the context of machine learning, refers to a set of techniques and methods applied to the output of a machine learning model in order to improve or refine its results. This may include steps such as data transformation, calibration, and thresholding. Post-processing is often used to enhance model performance, interpretability, and reliability when deployed in real-world applications. ==Purpose of P...")
13:26, 18 March 2023 Walle talk contribs created page Positive class (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the term '''positive class''' refers to one of the two possible outcomes in a binary classification problem. Binary classification is a type of supervised learning where the objective is to categorize a given input into one of two mutually exclusive categories or classes. These classes are often labeled as the positive and negative classes. The positive class is typically the t...")
13:26, 18 March 2023 Walle talk contribs created page Pipelining (Created page with "{{see also|Machine learning terms}} ==Pipelining in Machine Learning== Pipelining in machine learning refers to the process of chaining together multiple steps of a machine learning workflow, from data preprocessing and feature extraction to model training and evaluation, to create an efficient and organized end-to-end solution. Pipelining is commonly used to simplify the implementation, facilitate the management, and improve the reproducibility of complex machine learni...")
13:26, 18 March 2023 Walle talk contribs created page Parameter (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''parameter''' refers to a variable that is adjusted during the model training process to minimize errors and improve the accuracy of the model's predictions. These parameters enable the model to learn from data and represent the relationship between input features and target outputs. This article will provide an overview of parameters in machine learning, including their role in th...")
13:26, 18 March 2023 Walle talk contribs created page Pandas (Created page with "{{see also|Machine learning terms}} ==Introduction== Pandas is a widely used, open-source data manipulation and analysis library in Python that provides flexible, high-performance data structures for efficient handling of large and complex datasets. Although not specifically designed for machine learning, it has become an essential tool for data preprocessing, cleaning, and transformation tasks in the Machine Learning pipeline. ==Fe...")
13:25, 18 March 2023 Walle talk contribs created page Overfitting (Created page with "{{see also|Machine learning terms}} ==Overfitting in Machine Learning== ===Definition=== Overfitting is a phenomenon that occurs in machine learning when a model becomes excessively tailored to the training dataset, resulting in a decrease in its generalization performance on unseen data. In essence, the model learns the noise and peculiarities present in the training data, which negatively impacts its ability to make accurate predictions for new, unseen data. Overfittin...")
13:25, 18 March 2023 Walle talk contribs created page Output layer (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning and artificial neural networks, the '''output layer''' is a crucial component that translates the computational results from the hidden layers into meaningful and interpretable predictions or classifications. The output layer, consisting of one or more neurons, is responsible for generating the final output of a neural network model, which can be used for various purposes,...")
13:25, 18 March 2023 Walle talk contribs created page Online inference (Created page with "{{see also|Machine learning terms}} ==Online Inference in Machine Learning== ===Overview=== Online inference in machine learning refers to the process of making predictions or drawing conclusions in real-time based on new data, as opposed to relying on a pre-trained model. This approach is commonly employed in situations where data is received incrementally and predictions must be made promptly, such as in recommendation systems, financial markets, or real-time computer...")
13:25, 18 March 2023 Walle talk contribs created page One-vs.-all (Created page with "{{see also|Machine learning terms}} ==One-vs.-All in Machine Learning== One-vs.-all (OvA), also known as one-vs.-rest (OvR) or one-against-all, is a multi-class classification strategy commonly used in machine learning. It is a method for training a classifier to distinguish between multiple classes by converting the multi-class problem into several binary classification problems. The key idea is to train a separate binary classifier for each class, treating it as the po...")
13:25, 18 March 2023 Walle talk contribs created page One-hot encoding (Created page with "{{see also|Machine learning terms}} ==One-Hot Encoding== One-hot encoding is a widely used technique in the field of machine learning and data preprocessing. It is employed to convert categorical variables into a numerical format that is suitable for machine learning algorithms to process. This method involves transforming a categorical variable into a binary vector, where each category is represented by a unique combination of zeros and ones. ===Background=== C...")
13:25, 18 March 2023 Walle talk contribs created page Offline inference (Created page with "{{see also|Machine learning terms}} ==Offline Inference in Machine Learning== Offline inference, also known as batch inference, is a process in machine learning whereby a trained model is used to make predictions on a dataset in a non-interactive or non-real-time manner. This approach allows for the efficient processing of large datasets, as it does not require an immediate response to user inputs. ===Characteristics of Offline Inference=== Offline inference is char...")
13:24, 18 March 2023 Walle talk contribs created page Offline (Created page with "{{see also|Machine learning terms}} ==Offline Machine Learning== Offline machine learning, also known as batch learning or learning from static data, is a type of machine learning methodology where an algorithm is trained on a fixed dataset before deployment, rather than continuously updating its knowledge based on new data. In this approach, the model's training and testing phases are separate, and the model's generalization capabilities are of utmost importance...")
13:24, 18 March 2023 Walle talk contribs created page Numerical data (Created page with "{{see also|Machine learning terms}} ==Introduction== Numerical data, also referred to as quantitative data, is a type of data used extensively in machine learning and other computational disciplines. It consists of data points that can be represented and manipulated using numbers. In machine learning, numerical data is particularly important as it forms the foundation for mathematical models and algorithms that learn patterns and make predictions. This article will d...")
13:24, 18 March 2023 Walle talk contribs created page Normalization (Created page with "{{see also|Machine learning terms}} ==Normalization in Machine Learning== Normalization is a crucial preprocessing step in machine learning that aims to scale features or data points to a standardized range or distribution. By transforming the data to a common scale, it helps machine learning algorithms converge faster and achieve better performance. This is particularly important for algorithms that are sensitive to the scale of input features, such as gradient descen...")
13:24, 18 March 2023 Walle talk contribs created page Nonstationarity (Created page with "{{see also|Machine learning terms}} ==Introduction== Nonstationarity is a significant concept in the field of machine learning and statistics, which refers to the phenomenon where the underlying properties of a data-generating process change over time. In many real-world problems, the data encountered by machine learning models are subject to such changes, making it challenging to develop algorithms that can adapt and maintain their performance. In this article,...")
13:24, 18 March 2023 Walle talk contribs created page Nonlinear (Created page with "{{see also|Machine learning terms}} ==Introduction== Nonlinear methods in machine learning refer to a class of algorithms and techniques that are designed to model complex relationships between input and output variables, which cannot be adequately captured by linear models. These nonlinear models are particularly useful in situations where the underlying relationships between variables are more intricate or involve higher-order interactions. In this article, we will dis...")
13:24, 18 March 2023 Walle talk contribs created page Node (neural network) (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a node, also known as a neuron or unit, is a fundamental component of a neural network. It is responsible for receiving, processing, and transmitting information within the network. The functioning of nodes is inspired by the biological neurons found in the human brain, although the two differ significantly in their complexity and operation. ==Structure and Function== ===Input...")
13:24, 18 March 2023 Walle talk contribs created page Neuron (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''neuron''' refers to an elementary unit within an artificial neural network (ANN). These units, also known as nodes or artificial neurons, are inspired by biological neurons found in the nervous systems of living organisms. Neurons in ANNs serve to process and transmit information through the network, enabling various machine learning tasks such as classification, regression, and p...")
13:23, 18 March 2023 Walle talk contribs created page 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...")
13:23, 18 March 2023 Walle talk contribs created page 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...")
13:23, 18 March 2023 Walle talk contribs created page 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...")
13:23, 18 March 2023 Walle talk contribs created page 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...")
13:23, 18 March 2023 Walle talk contribs created page 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...")
13:23, 18 March 2023 Walle talk contribs created page 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,...")
13:22, 18 March 2023 Walle talk contribs created page 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...")
13:22, 18 March 2023 Walle talk contribs created page 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...")
13:21, 18 March 2023 Walle talk contribs created page 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...")
13:21, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:19, 18 March 2023 Walle talk contribs created page 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...")
13:18, 18 March 2023 Walle talk contribs created page 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-...")
13:16, 18 March 2023 Walle talk contribs created page 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...")