Titlebook: Machine Learning for Economics and Finance in TensorFlow 2; Deep Learning Models Isaiah Hull Book 2021 Isaiah Hull 2021 Machine Learning.Da

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Theoretical Models,er, we explain how theoretical economic models can be defined and solved in TensorFlow. We also discuss the use of reinforcement learning as a means of solving models and briefly consider an example that involves deep Q-learning.

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oblems with an empirical dimension.Define and solve any mathMachine learning has taken time to move into the space of academic economics. This is because empirical research in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine

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TensorFlow 2, 2, which was a substantial departure from TensorFlow 1. In this chapter, we will introduce TensorFlow 2, explain how it can be used in economics and finance, and then review preliminary material that will be necessary for understanding the material in later chapters. .

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Trees,lems in economics and finance. In this chapter, we introduce the concept of tree-based models, including random forests and gradient-boosted trees, and then examine their implementation in the high-level Estimators API.

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Time Series,n. There is, however, a clear intersection between objectives when it comes to forecasting in economics and finance. Throughout this chapter, we will use machine learning and TensorFlow to forecast inflation in a time series context, building on an early use of neural networks in economics (Nakamura 2005).

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Isaiah HullGain a full pipeline of tools needed to structure and develop an ML economics project.Apply a variety of deep learning models to economic problems with an empirical dimension.Define and solve any math

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https://doi.org/10.1007/978-1-4842-6373-0Machine Learning; Data Science; Big Data; Economics; Finance; TesnorFlow; Deep Learning; Text Analysis; Natu