| 书目名称 | Machine Learning for Economics and Finance in TensorFlow 2 | | 副标题 | Deep Learning Models | | 编辑 | Isaiah Hull | | 视频video | | | 概述 | Gain 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 | | 图书封面 |  | | 描述 | Machine 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 learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for students, academics, and professionals who lack a standard reference on machine learning for economics and finance..This book focuses on economic and financial problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, LSTMs, and DQNs), generative machine learning models (GANs and VAEs), and tree-based models. It also covers the intersection of empirical methods in economics and machine learning, including regression analysis, natural language processing, and dimensionality reduction..TensorFlow offers a toolset that can be used to define and solve any graph-based model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each frame | | 出版日期 | Book 2021 | | 关键词 | Machine Learning; Data Science; Big Data; Economics; Finance; TesnorFlow; Deep Learning; Text Analysis; Natu | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-6373-0 | | isbn_softcover | 978-1-4842-6372-3 | | isbn_ebook | 978-1-4842-6373-0 | | copyright | Isaiah Hull 2021 |
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发表于 2025-3-21 19:02:49
