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001432711 001__ 1432711
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001432711 020__ $$a9781484263730$$q(electronic bk.)
001432711 020__ $$a1484263731$$q(electronic bk.)
001432711 020__ $$z1484263723
001432711 020__ $$z9781484263723
001432711 0247_ $$a10.1007/978-1-4842-6373-0$$2doi
001432711 035__ $$aSP(OCoLC)1224579675
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001432711 049__ $$aISEA
001432711 050_4 $$aQ325.5$$b.H85 2021
001432711 08204 $$a006.3/1$$223
001432711 1001_ $$aHull, Isaiah,$$eauthor.
001432711 24510 $$aMachine learning for economics and finance in TensorFlow 2 :$$bdeep learning models for research and industry /$$cIsaiah Hull.
001432711 264_1 $$aBerkeley, CA :$$bApress,$$c[2021]
001432711 300__ $$a1 online resource :$$billustrations (some color)
001432711 336__ $$atext$$btxt$$2rdacontent
001432711 337__ $$acomputer$$bc$$2rdamedia
001432711 338__ $$aonline resource$$bcr$$2rdacarrier
001432711 347__ $$atext file
001432711 347__ $$bPDF
001432711 504__ $$aIncludes bibliographical references and index.
001432711 5050_ $$aChapter 1: TensorFlow 2.0 -- Chapter 2: Machine Learning and Economics -- Chapter 3: Regression -- Chapter 4: Trees -- Chapter 5: Image Classification -- Chapter 6: Text Data -- Chapter 7: Time Series -- Chapter 8: Dimensionality Reduction -- Chapter 9: Generative Models -- Chapter 10: Theoretical Models.
001432711 506__ $$aAccess limited to authorized users.
001432711 520__ $$aFind solutions to problems in economics and finance using tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, and DQNs), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal component analysis. TensorFlow offers a toolset that can be used to set up and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. You will: Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance.
001432711 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 9, 2021).
001432711 63000 $$aTensorFlow.
001432711 650_0 $$aMachine learning.
001432711 650_0 $$aApplication software$$xDevelopment.
001432711 650_6 $$aApprentissage automatique.
001432711 650_6 $$aLogiciels d'application$$xDéveloppement.
001432711 655_0 $$aElectronic books.
001432711 77608 $$iPrint version:$$aHull, Isaiah.$$tMachine learning for economics and finance in TensorFlow 2.$$dBerkeley, CA : Apress, [2021]$$z9781484263723$$w(OCoLC)1195461084
001432711 852__ $$bebk
001432711 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6373-0$$zOnline Access$$91397441.1
001432711 909CO $$ooai:library.usi.edu:1432711$$pGLOBAL_SET
001432711 980__ $$aBIB
001432711 980__ $$aEBOOK
001432711 982__ $$aEbook
001432711 983__ $$aOnline
001432711 994__ $$a92$$bISE