ISBN: 9781032628714 - machine learning in farm animal behavior using python (6 resultados)

Paperback. Condición: Brand New. 394 pages. 6.14x0.93x9.21 inches. In Stock.

Condición: New. Natasa Kleanthous holds a BSc in Management and Information Systems from the University of Nicosia, and an MSc in Computing and Information Systems from Liverpool John Moores University, UK. She earned her PhD from Liverpool John Moores .

Taschenbuch. Condición: Neu. Neuware - This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis. The book includes detailed Python examples for each phase, making it an essential resource for researchers and practitioners in animal behavior and technology. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…