Machine Learning for Economics and Finance in TensorFlow 2

Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry

eBook Details:

  • Paperback: 384 pages
  • Publisher: WOW! eBook (November 26, 2020)
  • Language: English
  • ISBN-10: 1484263723
  • ISBN-13: 978-1484263723

eBook Description:

Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry

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.

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 workhorse models in economics and finance

TensorFlow offers a toolset that can be used to set up and solve any mathematical model, including those commonly used in economics. This Machine Learning for Economics and Finance in TensorFlow 2 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.

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3 Responses

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  3. March 17, 2021

    […] Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. […]

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