Behavior Analysis with Machine Learning Using R

von

Ceja, Enrique Garcia

BuchKartoniert, Paperback

Verkaufsrang3735inPsychologie

CHF79.90

BuchabCHF79.90

E-BookabCHF80.60

In den Warenkorb

Beschreibung

This book introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records.

Weitere Beschreibungen

Details

ISBN/GTIN978-1-032-06705-6

ProduktartBuch

EinbandKartoniert, Paperback

VerlagTaylor and FrancisChapman and Hall/CRC

Erscheinungsdatum29.01.2024

Auflage1. A.

ReiheChapman & Hall/CRC The R Series

Seiten400 Seiten

SpracheEnglisch

MasseBreite 156 mm, Höhe 234 mm

Gewicht800 g

IllustrationenFarb., s/w. Abb.

Artikel-Nr.50850448

KatalogBuchzentrum

Datenquelle-Nr.45634325

WarengruppePsychologie

Weitere Details

Reihe

Dealing with Memory Limits and Working with Large Data Sets in R

Statistical Computing with R

R Graphics

Statistical Computing in C++ and R

Event History Analysis with R

Customer and Business Analytics

Programming Graphical User Interfaces in R

Analyzing Baseball Data with R

Implementing Reproducible Research

Stated Preference Methods Using R

Growth Curve Analysis and Visualization Using R

Computational Actuarial Science with R

Multiple Factor Analysis by Example Using R

Analyzing Sensory Data with R

Displaying Time Series, Spatial, and Space-Time Data with R

Using R for Numerical Analysis in Science and Engineering

Advanced R

Introduction to Scientific Programming and Simulation Using R

Using R for Introductory Statistics

Data Science in R

Parallel Computing for Data Science

Dynamic Documents with R and knitr

Statistics in Toxicology Using R

Graphical Data Analysis with R

Reproducible Research with R and R Studio

Introductory Fisheries Analyses with R

Using the R Commander

bookdown

Spatial Microsimulation with R

The Essentials of Data Science: Knowledge Discovery Using R

R Primer

Dynamic Documents with R and knitr

Reproducible Research with R and R Studio

Extending R

bookdown

Using the R Commander

Customer and Business Analytics

R Primer

blogdown

Flexible Regression and Smoothing

Data Science in R

R Markdown

Reproducible Finance with R

Practical R for Mass Communication and Journalism

Displaying Time Series, Spatial, and Space-Time Data with R

Statistical Computing with R, Second Edition

Hands-On Machine Learning with R

Advanced R, Second Edition

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Reproducible Research with R and RStudio

Learn R

Learning Microeconometrics with R

Geocomputation with R

Digital Media

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Interactive Documents with R

Engineering Production-Grade Shiny Apps

Javascript for R

Advanced R Solutions

Dose-Response Analysis Using R

Statistics in Toxicology Using R

Distributions for Modeling Location, Scale, and Shape

Event History Analysis with R

Outstanding User Interfaces with Shiny

Rasch Measurement Theory Analysis in R

Spatial Sampling with R

A Criminologist's Guide to R

R for Political Data Science

ANOVA and Mixed Models

Open Science with R

Spatial Data Science

Starting Quantitative Analysis with R

Analyzing US Census Data

Deep Learning and Scientific Computing with R torch

Tidy Finance with R

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Graphical Data Analysis with R

Statistical Computing in C++ and R

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Modern Data Visualization with R

Learn R