Name: Multimodal Deep Learning for Poverty Prediction (en Inglés)
SKU: 68946222
Price: 37.21 USD
Availability: InStock
Author: Christian Brown
ISBN: 9798182713115

portada Multimodal Deep Learning for Poverty Prediction (en Inglés)

Multimodal Deep Learning for Poverty Prediction (en Inglés)

Formato

Libro Físico

Editorial

Nala Kala

Autor

Christian Brown

Idioma

Inglés

N° páginas

150

Encuadernación

Tapa Blanda

ISBN13

9798182713115

Multimodal Deep Learning for Poverty Prediction (en Inglés)

Christian Brown (Autor) · Nala Kala · Tapa Blanda

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Reseña del libro "Multimodal Deep Learning for Poverty Prediction (en Inglés)"

Multimodal Deep Learning for Poverty Prediction examines the application of deep learning and multimodal data analysis to the prediction and assessment of poverty-related conditions. The book explores how information from different data modalities can be integrated to identify patterns, relationships, and indicators associated with socioeconomic conditions. It introduces fundamental concepts in deep learning, multimodal machine learning, data fusion, predictive modeling, and socioeconomic data analytics. Attention is given to methods for combining heterogeneous information sources, extracting meaningful representations, developing predictive models, and evaluating their performance. The discussion considers important analytical factors such as data quality, feature representation, model learning, classification, prediction, and interpretation of results. By connecting multimodal artificial intelligence with poverty prediction, the book provides a technical perspective on how computational methods can support quantitative socioeconomic analysis. It is intended for students, researchers, data scientists, economists, social scientists, engineers, and professionals interested in artificial intelligence, machine learning, data analytics, development studies, and computational approaches to socioeconomic research. The book provides a foundation for understanding multimodal deep learning techniques and their role in data-driven poverty analysis.