Machine Learning for Economics and Finance in TensorFlow 2 : Deep Learning Models for Research and Industry
1st ed.
by
Isaiah Hull
Book Details
Format
Paperback / Softback
ISBN-10
1484263723
ISBN-13
9781484263723
Edition
1st ed.
Publisher
APress
Imprint
APress
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Nov 26th, 2020
Print length
368 Pages
Weight
588 grams
Dimensions
15.50 x 23.40 x 2.50 cms
Product Classification:
Artificial intelligenceArtificial intelligence (AI)
Ksh 9,900.00
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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 framed in terms of a specific economic problem of interest or topic. This simplifies otherwise complicated concepts, enabling the reader to solve workhorse theoretical models in economics and finance using TensorFlow.
What You''ll Learn
- 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 theoretical models in economics
Who This Book Is For
Students, data scientists working in economics and finance, public and private sector economists, and academic social scientists
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