18 March 2023
- 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