Machine Learning in Farm Animal Behavior using Python
- Publisher's listprice GBP 170.00
-
86 037 Ft (81 940 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 10% (cc. 8 604 Ft off)
- Discounted price 77 433 Ft (73 746 Ft + 5% VAT)
86 037 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 6 March 2025
- ISBN 9781032628639
- Binding Hardback
- No. of pages412 pages
- Size 234x156 mm
- Weight 920 g
- Language English
- Illustrations 62 Illustrations, black & white; 6 Illustrations, color; 62 Line drawings, black & white; 6 Line drawings, color 693
Categories
Short description:
The book includes detailed Python examples for each phase, making it an essential resource for researchers and practitioners in animal behavior and technology.
MoreLong description:
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.
MoreTable of Contents:
Preface. 1. Introduction to Machine Learning for Farm Animal Behavior 2. Machine Learning Concepts and Challenges. 3. A Practical Example to Building a Simple Machine Learning Model 4. Sensors, Data Collection, and Annotation 5. Preprocessing and Feature Extraction for Animal Behavior Research 6. Feature Selection Techniques 7. Animal Research: Supervised and Unsupervised Learning Algorithms 8. Evaluation, Model Selection and Hyperparameter Tuning 9. Deep Learning Algorithms for Animal Activity Recognition References
More
