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- Nested Models Description: Nested models are statistical structures that are hierarchically related to each other, allowing the inclusion of variables at(...) Read more
- Non-homogeneous Poisson Process Description: The Non-Homogeneous Poisson Process is an extension of the classic Poisson process, where the rate of occurrence of events is not(...) Read more
- Non-stationary Time Series Description: A non-stationary time series is a set of data collected over time whose statistical properties, such as mean and variance, change(...) Read more
- Neural Style Transfer Description: Neural style transfer is a technique used to combine two images by applying the style of one image to the content of another. This(...) Read more
- Neural Turing Machine Description: A neural Turing machine is a type of neural network that combines a neural network with an external memory matrix. This innovative(...) Read more
- Numerical Optimization Description: Numerical optimization is the process of finding the best solution from a set of feasible solutions using numerical methods. This(...) Read more
- Neighborhood Description: In the context of machine learning and data science, the term 'neighborhood' refers to the data points surrounding a specific point(...) Read more
- Neural Code Description: Neural code refers to the representation of data in a neural network. This concept is fundamental in the field of deep learning,(...) Read more
- Non-parametric Statistics Description: Non-parametric statistics refers to a set of statistical methods that do not require data to follow a specific distribution. Unlike(...) Read more
- Neural Data Description: Neural data refers to data that is generated or processed by neural networks, a type of computational model inspired by the(...) Read more
- Normalization of Features Description: Feature normalization is a fundamental process in data preprocessing that involves scaling the features of a dataset so that they(...) Read more
- Normalization of Time Series Description: Time series normalization is the process of adjusting time series data to remove trends and seasonality, allowing for more(...) Read more
- Normalization of Input Data Description: Input data normalization is a crucial process in data preprocessing that involves scaling the values of features in a dataset to(...) Read more
- Numerical Encoding Description: Numerical encoding is the process of converting categorical variables into a numerical format that can be used in machine learning(...) Read more
- Noise Filtering Description: Noise filtering is the process of removing unwanted noise from data to improve the quality of information. This concept is(...) Read more
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