headerdesktop zminitr16mai25

MAI SUNT 2:10:10:48

X

headermobile zminitr16mai25

MAI SUNT 2:10:10:48

X

Promotii popup img

🤩Târgul Mini-Cititorilor🥳

Transport Gratuit la peste 75 lei

-84%, -30%, -50%, -40%

Comandă acum👉

Behavior Analysis with Machine Learning Using R

Behavior Analysis with Machine Learning Using R - Enrique Garcia Ceja

Behavior Analysis with Machine Learning Using R

Behavior Analysis with Machine Learning Using R 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. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial.

Features:

  • Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on.

  • Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources.

  • Use unsupervised learning algorithms to discover criminal behavioral patterns.

  • Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images.

  • Evaluate the performance of your models in traditional and multi-user settings.

  • Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors.

This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

Citeste mai mult

-10%

transport gratuit

PRP: 1023.00 Lei

!

Acesta este Pretul Recomandat de Producator. Pretul de vanzare al produsului este afisat mai jos.

920.70Lei

920.70Lei

1023.00 Lei

Primesti 920 puncte

Important icon msg

Primesti puncte de fidelitate dupa fiecare comanda! 100 puncte de fidelitate reprezinta 1 leu. Foloseste-le la viitoarele achizitii!

Indisponibil

Descrierea produsului

Behavior Analysis with Machine Learning Using R 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. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial.

Features:

  • Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on.

  • Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources.

  • Use unsupervised learning algorithms to discover criminal behavioral patterns.

  • Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images.

  • Evaluate the performance of your models in traditional and multi-user settings.

  • Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors.

This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

Citeste mai mult

Parerea ta e inspiratie pentru comunitatea Libris!

Acum se comanda

Noi suntem despre carti, si la fel este si

Newsletter-ul nostru.

Aboneaza-te la vestile literare si primesti un cupon de -10% pentru viitoarea ta comanda!

*Reducerea aplicata prin cupon nu se cumuleaza, ci se aplica reducerea cea mai mare.

Ma abonez image one
Ma abonez image one