Isaiah Hull – författare

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 LearnDefine, train, and evaluate machine learning models in TensorFlow 2Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve theoretical models in economicsWho This Book Is ForStudents, data scientists working in economics and finance, public and private sector economists, and academic social scientists

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 LearnDefine, train, and evaluate machine learning models in TensorFlow 2Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve theoretical models in economicsWho This Book Is ForStudents, data scientists working in economics and finance, public and private sector economists, and academic social scientists

This book offers an introduction to quantum technology that is specifically tailored to economists, students of economics, and professionals in the financial and payments industries. The book reviews quantum speedups that have been identified for algorithms used to solve and estimate economic models, including function approximation, linear systems analysis, graphical modeling, Monte Carlo simulation, matrix inversion, principal component analysis, linear regression, dynamic programming, interpolation, numerical differentiation, and true random number generation. It also provides an overview of quantum financial technology and its potential applications in economics and finance. Written by an interdisciplinary team with backgrounds in economics, computer science, and physics, this book offers a valuable guide for researchers and practitioners who want to understand the implications and possibilities of quantum technology for the field of economics.

This book offers an introduction to quantum technology that is specifically tailored to economists, students of economics, and professionals in the financial and payments industries. The book reviews quantum speedups that have been identified for algorithms used to solve and estimate economic models, including function approximation, linear systems analysis, graphical modeling, Monte Carlo simulation, matrix inversion, principal component analysis, linear regression, dynamic programming, interpolation, numerical differentiation, and true random number generation. It also provides an overview of quantum financial technology and its potential applications in economics and finance. Written by an interdisciplinary team with backgrounds in economics, computer science, and physics, this book offers a valuable guide for researchers and practitioners who want to understand the implications and possibilities of quantum technology for the field of economics.