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portada Multimodal Deep Learning for Poverty Prediction
Type
Physical Book
Publisher
Author
Language
English
Pages
150
Format
Paperback
ISBN13
9798182713115

Multimodal Deep Learning for Poverty Prediction

Christian Brown (Author) · Nala Kala · Paperback

Multimodal Deep Learning for Poverty Prediction - Christian Brown

New Book Imported to Taiwan
Delivery: 30 Oct - 09 Nov Shipping: 5 to 6 business days.
NT$ 1,065
NT$ 1,065

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Synopsis "Multimodal Deep Learning for Poverty Prediction"

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.

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The binding of this edition is Paperback.

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