Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python is a book that 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. It is suitable for both teaching and research and introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI).

Format: Paperback / softback
Length: 248 pages
Publication date: 13 June 2022
Publisher: Taylor & Francis Ltd

Statistical Learning using Neural Networks: A Comprehensive Guide for Statisticians and Data Scientists with Python delves into the realm of artificial neural networks, providing a comprehensive and accessible introduction for both statisticians and data scientists. Starting from the fundamentals, the book gradually assumes a more advanced level of expertise, challenging readers to delve deeper into the theory and its practical applications in statistical methods. With an emphasis on concrete Python code examples, readers can grasp the intricacies of neural networks and apply them to various research areas.

The book offers a diverse range of widely employed statistical methodologies, showcased through practical Python code examples, which are readily accessible online. Its suitability extends to scientists, developers, and graduate students alike, equipping them with the knowledge and tools necessary to excel in their respective fields.

Key Features:

Comprehensive Coverage: The book explores a wide array of widely used statistical methodologies, spanning various research areas. It provides practical insights into topics such as Multivariate Statistics, Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.

Suitable for Teaching and Research: Designed to serve both teaching and research purposes, this book offers a solid foundation for understanding neural networks and their applications. It caters to outsiders of academia who are engaged in data mining and artificial intelligence (AI), providing a seamless bridge between statistics and computer science.

Integration of Data Analysis: The book brings together data analysis from the realms of statistics and computer science, utilizing neural networks as powerful tools. It showcases how neural networks can be employed to solve complex problems, enhance model performance, and extract valuable insights from data.

In-depth Coverage of Neural Network Models: The book provides detailed explanations and examples of various neural network models, including Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders. Readers gain a deep understanding of these models' architectures, training procedures, and applications in statistical learning.

Accessible and Well-Structured: Written in a clear and concise manner, the book is well-organized and easy to follow. It includes numerous illustrations, code snippets, and exercises to reinforce the concepts and facilitate a hands-on learning experience.

Whether you are a seasoned statistician seeking to expand your knowledge of neural networks or a data scientist looking to incorporate these powerful tools into your work, Statistical Learning using Neural Networks: A Comprehensive Guide for Statisticians and Data Scientists with Python is an invaluable resource. By leveraging the power of artificial intelligence and statistical learning, you can unlock new possibilities and advance your understanding of data analysis and machine learning.

Weight: 460g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032335933