Images as data for social science research : an introduction to convolutional neural nets for image classification
著者
書誌事項
Images as data for social science research : an introduction to convolutional neural nets for image classification
（Cambridge elements, . Elements in quantitative and computational methods for the social sciences / edited by R. Michael Alvarez,
Cambridge University Press, 2020
- : pbk
- タイトル別名
-
Quantitative and computational methods for the social sciences
大学図書館所蔵 件 / 全3件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references
内容説明・目次
内容説明
Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.
目次
- 1. Introduction
- 2. Prerequisites for computer vision methods and tutorials
- 3. Introduction to CNNs for social scientists
- 4. Overview of fine-tuning a CNN classifier for images
- 5. Political science working example: images related to a Black Lives Matter protest
- 6. The promise and limits of autotaggers
- 7. Application: fine-tuning an open source CNN
- 8. Legal and ethical concerns in using images as data
- 9. Conclusion
- 10. References.
「Nielsen BookData」 より