Deep learning for time series data /
"Arun Kejariwal (Independent) and Ira Cohen (Anodot) share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. The first step uses anomaly detection algorithms to discover anomalies in a time series in the training data. In the second, multip...
Clasificación: | Libro Electrónico |
---|---|
Autor Corporativo: | O'Reilly Artificial Intelligence Conference |
Formato: | Electrónico Congresos, conferencias Video |
Idioma: | Inglés |
Publicado: |
[Place of publication not identified] :
O'Reilly,
2019.
|
Temas: | |
Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Ejemplares similares
-
Using AutoML to automate selection of machine learning models and hyperparameters /
Publicado: (2019) -
Leveraging entity-resolution to identify customers in 3rd party data /
Publicado: (2020) -
Managing data science : effective strategies to manage data science projects and build a sustainable team /
por: Dubovikov, Kirill
Publicado: (2019) -
Managing data science : effective strategies to manage data science projects and build a sustainable team /
por: Dubovikov, Kirill
Publicado: (2019) -
Hands-on data science with Anaconda : utilize the right mix of tools to create high-performance data science applications /
por: Yan, Yuxing, et al.
Publicado: (2018)