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Deep Learning Models for Economic Research .pdf
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内容特别丰富，而且非常新，很有参考价值。全部矢量文字，适合机翻。
In today’s data-driven world, the ability to make sense of complex, highdimensional datasets is crucial for economists and data scientists. Traditional quantitative methods, while powerful, often struggle to keep up with the complexities of modern economic challenges. This resource bridges this gap, integrating cutting-edge machine learning techniques with established economic analysis to provide new, more accurate insights.
The resource offers a comprehensive approach to understanding and applying neural networks and deep learning models in the context of conducting economic research. It starts by laying the groundwork with essential quantitative methods such as cluster analysis, regression, and factor analysis, then emonstrates how these can be enhanced with deep learning techniques like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. By guiding readers through real-world examples, complete with Python code and access to datasets, it showcases the practical benefits of neural networks in solving complex conomic problems, such as fraud detection, sentiment analysis, stock price forecasting, and inflation factor analysis. Importantly, the resource also addresses critical concerns about the “black box” nature of deep learning, offering interpretability techniques like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to demystify model predictions. The resource is essential reading for economists, data scientists, and professionals looking to deepen their understanding of AI’s role in economic modeling. It is also an accessible resource for non-experts interested in how machine learning is transforming economic analysis.
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