Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python
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Author(s): Pereira, Basilio de Braganoca;Rao, C Radhakrishna(Contributor);Oliveira, Fabio Borges De(Contributor)
Publisher: CRC Press, Year: 2020
Description:
Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Pythonintroduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.
Key Features:
Discusses applications in several research areas
Covers a wide range of widely used statistical methodologies
Includes Python code examples
Gives numerous neural network models
This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.
This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Pythonintroduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.
Key Features:
Discusses applications in several research areas
Covers a wide range of widely used statistical methodologies
Includes Python code examples
Gives numerous neural network models
This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.
This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
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