E-Book (pdf) Machine Learning for Economics and Finance in TensorFlow 2 von Isaiah Hull
CHF 65.85 67.90
Download steht sofort bereit

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.
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 theoretical models in economics



Autorentext
Isaiah Hull received his PhD in Economics from Boston College in 2013 and has since worked in the Research Division at Sweden's Central Bank. He has published numerous articles in academic journals primarily concentrated in computational economics with applications in macroeconomics, finance, and housing. Most of his recent work makes use of techniques from machine learning. He also regularly presents at conferences on machine learning and big data in economics. And Isaiah is an accomplished teacher with experience teaching TensorFlow 2.0. Currently, he's working on a project to introduce quantum computing to economists.

Inhalt

Chapter 1: TensorFlow 2.0

Chapter Goal: Introduce TensorFlow 2 and discuss preliminary material on conventions and practices specific to TensorFlow. 

·      Differences between TensorFlow iterations

·      TensorFlow for economics and finance

·      Introduction to tensors

·      Review of linear algebra and calculus

·      Loading data for use in TensorFlow

·      Defining constants and variables

 

 

 

Chapter 2: Machine Learning and Economics

Chapter Goal: Provide a high-level overview of machine learning models and explain how they can be employed in economics and finance. Part of the chapter will review existing work in economics and speculate on future use-cases.

·      Introduction to machine learning

·      Machine learning for economics and finance

·      Unsupervised machine learning

·      Supervised machine learning

·      Regularization

·      Prediction

·      Evaluation

 

Chapter 3: Regression

Chapter Goal: Explain how regression models are used primarily for prediction purposes in machine learning, rather than hypothesis testing, as is the case in economics. Introduce evaluation metrics and optimization routines used to solve regression models. 

·      Linear regression

·      Partially-linear regression

·      Non-linear regression

·      Logistic regression

·      Loss functions

·      Evaluation metrics

·      Optimizers

 

Chapter 4: Trees

Chapter Goal: Introduce tree-based models and the concept of ensembles.

·      Decision trees

·      Regression trees

·      Random forests

·      Model tuning

 

Chapter 5: Gradient Boosting

Chapter Goal: Introduce gradient boosting and discuss how it is applied, how models are tuned, and how to identify important features.

·      Introduction to gradient boosting

·      Boosting with regression models

·      Boosting with trees

·     &am...

Titel
Machine Learning for Economics and Finance in TensorFlow 2
Untertitel
Deep Learning Models for Research and Industry
Autor
Isaiah Hull
EAN
9781484263730
Format
E-Book (pdf)
Hersteller
Apress
Genre
IT & Internet
Digitaler Kopierschutz
Wasserzeichen
Dateigrösse
5.87 MB
Anzahl Seiten
368