Machine learning for economics and finance in TensorFlow 2 : deep learning models for research and industry / Isaiah Hull.
Hull, Isaiah, author.
2021
Q325.5 .H85 2021
Available Online

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Title Machine learning for economics and finance in TensorFlow 2 : deep learning models for research and industry / Isaiah Hull.
Author Hull, Isaiah, author.
ISBN 9781484263730 (electronic bk.)
1484263731 (electronic bk.)
1484263723
9781484263723
DOI https://doi.org/10.1007/978-1-4842-6373-0
Published Berkeley, CA : Apress, [2021]
Language English
Description 1 online resource : illustrations (some color)
Item Number 10.1007/978-1-4842-6373-0 doi
Call Number Q325.5 .H85 2021
Dewey Decimal Classification 006.3/1
Summary Find solutions to problems in economics and finance using tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work 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 both students and professionals in the economics industry without a standard reference. This book focuses on economic 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, RNNs, LSTMs, and DQNs), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal component analysis. TensorFlow offers a toolset that can be used to set up and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. You will: Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
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PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed February 9, 2021).
Available in Other Form Machine learning for economics and finance in TensorFlow 2.
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Chapter 1: TensorFlow 2.0
Chapter 2: Machine Learning and Economics
Chapter 3: Regression
Chapter 4: Trees
Chapter 5: Image Classification
Chapter 6: Text Data
Chapter 7: Time Series
Chapter 8: Dimensionality Reduction
Chapter 9: Generative Models
Chapter 10: Theoretical Models.

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