Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Multimodal Deep Learning for Poverty Prediction

Multimodal Deep Learning for Poverty Prediction

Brown, Christian

Paperback

AgricultureDatabasesGeneral Computers

Currently unavailable to order

ISBN13: 9798182713115
Publisher: Nala Kala
Pages: 150
Weight: 0.46
Height: 0.32 Width: 6.00 Depth: 9.00
Language: English

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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