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  Attention Information Attention mechanisms are widely used in machine learning and natural language processing tasks to help model understand the relative importance of different parts of the input. The attention mechanism works by computing the similarity between a "query" and "key" vectors to determine the importance of the key for the given query. In an attention mechanism, the query vector represents .. Zettelkasten/Terminology Information 2023. 3. 19.

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  CNN-LSTM Information CNN-LSTM is a hybrid deep learning architecture that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. CNN-LSTM is designed for sequence prediction tasks, where the input is a time-series or spatiotemporal data. In the CNN-LSTM architecture, the CNN layer is used to extract local features from the input sequence, while the LSTM layer captures t.. Zettelkasten/Terminology Information 2023. 3. 18.

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  DA (Domain Adaptation) Information DA (Domain Adaptation) is a subfield of machine learning that aims to adapt models trained on a source domain to perform well on a target domain, where the distributions of the source and target data differ. The primary goal of DA is to improve the generalization of a model to new, unseen data, especially in scenarios where the target data is limited or expensive to collect. There ar.. Zettelkasten/Terminology Information 2023. 3. 18.

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  CNN (Convolutional Neural Network) Information CNN (Convolutional Neural Network) is a type of neural network that has been widely used for image and video processing tasks. CNNs consist of multiple layers that extract and transform features from input data. The core building block of a CNN is the convolutional layer, which applies a set of learnable filters to the input data. The filters convolve across the input data, computing.. Zettelkasten/Terminology Information 2023. 3. 17.

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  NRMSE (Normalized Root Mean Squared Error) Information NRMSE (Normalized Root Mean Squared Error) is a measure of the accuracy of a regression model, representing the ratio of the root mean squared error to the range of the dependent variable. It is used to compare the performance of different models or to evaluate the accuracy of a model over time. The formula for NRMSE is: NRMSE = RMSE / (y_max - y_min), where RMSE is the root mean squ.. Zettelkasten/Terminology Information 2023. 3. 17.

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  LSTM (Long Short-Term Memory) Information LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is specifically designed to capture long-term dependencies and handle the problem of vanishing gradients that often occur in traditional RNNs. It was introduced in 1997 by Hochreiter and Schmidhuber to solve the problem of learning and remembering sequences of information for a long time. LSTM.. Zettelkasten/Terminology Information 2023. 3. 16.

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  Tansig (hyperbolic tangent sigmoid) Information Tansig (hyperbolic tangent sigmoid) is a commonly used activation function in artificial neural networks. It is a type of sigmoid function that has an "S"-shaped curve, with a range of -1 to 1. The function is defined as f(x) = (2 / (1 + exp(-2x))) - 1. The tansig function maps any real-valued input to a corresponding output between -1 and 1. It is often used in hidden layers of neur.. 카테고리 없음 2023. 3. 16.

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  Multi-step ahead forecasting Information Multi-step ahead forecasting refers to the task of predicting multiple future time steps in a time series. This is a more challenging task than single-step forecasting, which involves predicting only the next time step. Multi-step ahead forecasting can be performed using a variety of techniques, including statistical models and machine learning algorithms. Some common approaches to m.. Zettelkasten/Terminology Information 2023. 3. 15.

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  Ensemble learning Information Ensemble learning combines the predictions of multiple models to achieve better accuracy than a single model. There are three types of ensemble learning: bagging, boosting, and stacking. Bagging (bootstrap aggregating) involves training multiple models on different subsets of the training data and then averaging their predictions. Boosting involves training multiple models in sequenc.. Zettelkasten/Terminology Information 2023. 3. 15.

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  Short receptive field Information A receptive field is a term used in machine learning to describe the size of the area in an image or sequence that the model is considering at one time. A short receptive field is a small area that a model considers at once. Short receptive fields are used in neural networks and deep learning models to enable the model to capture local patterns in the input data. When a model has a s.. Zettelkasten/Terminology Information 2023. 3. 14.

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  F-statistic Information The F-statistic is a measure used in statistical analysis to determine the significance of the difference between two group means. It is calculated by dividing the variance between groups by the variance within groups. F-statistic is used in analysis of variance (ANOVA), regression analysis, and other types of statistical models. The F-statistic follows the F-distribution, which is a.. Zettelkasten/Terminology Information 2023. 3. 14.

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  BiLSTM (Bidirectional Long Short-Term Memory) Information BiLSTM (Bidirectional Long Short-Term Memory) is a variant of LSTM (Long Short-Term Memory) neural networks that is commonly used for sequential data analysis tasks such as speech recognition, natural language processing, and time series prediction. BiLSTM models incorporate a forward LSTM and a backward LSTM that process the input sequence in both directions. The forward LSTM proces.. Zettelkasten/Terminology Information 2023. 3. 13.

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  P-value Information P-value is a statistical measure that helps to determine the significance of a result in a hypothesis test. It represents the probability of obtaining an observation or a more extreme observation, assuming the null hypothesis is true. The null hypothesis is the hypothesis that there is no significant difference between two groups or variables. The smaller the p-value, the more eviden.. Zettelkasten/Terminology Information 2023. 3. 13.

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  DCCN (Dilated Causal Convolution Neural Network) Information DCCN (Dilated Causal Convolution Neural Network) is a deep learning architecture used for time series forecasting that combines dilated causal convolutional layers and residual connections. Dilated causal convolutions are a variant of convolutional layers that incorporate time information and can capture long-term dependencies in time series data. The causal aspect of the convolution.. Zettelkasten/Terminology Information 2023. 3. 12.

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  CART (Classification And Regression Tree) Information CART (Classification And Regression Tree) is a decision tree algorithm used for both classification and regression analysis. It involves recursively splitting the dataset into smaller subsets by identifying the best predictor variables and splitting rules that minimize the variance or impurity within each subset. The algorithm builds a binary tree with the target variable at the root.. Zettelkasten/Terminology Information 2023. 3. 12.

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  DL (Deep Learning) Information Deep Learning is a subset of machine learning that uses neural networks with multiple hidden layers to learn and extract features from large amounts of data. The architecture of a deep neural network typically consists of several layers of neurons, each performing a different transformation on the input data. Deep learning models are capable of learning hierarchical representations o.. Zettelkasten/Terminology Information 2023. 3. 11.

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  Regression tree Information A regression tree is a decision tree used in regression problems, i.e., where the goal is to predict a continuous target variable. It is built using a recursive partitioning algorithm that splits the data into homogeneous subsets based on the values of the predictor variables. The algorithm finds the best split at each node using a criterion such as the sum of squared errors, varianc.. Zettelkasten/Terminology Information 2023. 3. 11.

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  NN (Neural Network) Information Neural networks are a class of machine learning algorithms that are inspired by the structure and function of the human brain. They consist of layers of interconnected nodes, or artificial neurons, that can learn to recognize patterns in data by adjusting the weights of the connections between them. A typical neural network consists of an input layer, one or more hidden layers, and a.. Zettelkasten/Terminology Information 2023. 3. 10.

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  PV (PhotoVoltaic) Information A PV (PhotoVoltaic) system is a type of renewable energy technology that converts sunlight into electricity. PV cells are made up of semiconducting materials, such as silicon, and are designed to produce a flow of electrons when exposed to sunlight. The electricity produced by a PV system can be used to power a variety of applications, from small consumer devices to large-scale power.. Zettelkasten/Terminology Information 2023. 3. 10.

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  GA (Genetic Algorithm) Information GA (Genetic Algorithm) is a metaheuristic optimization algorithm that is inspired by the biological process of natural selection and genetics. The algorithm starts by randomly initializing a population of individuals, each representing a potential solution to the optimization problem, and evaluating their fitness based on a predefined objective function. The individuals are then sele.. Zettelkasten/Terminology Information 2023. 3. 9.