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Item #:
144230785
Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch Second Edition
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Item #:
144230785
INR 5440
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What Stands Out
Hands-On Approach
The book offers practical, hands-on tutorials for implementing anomaly detection, empowering readers to apply deep learning techniques immediately in real-world scenarios.
Comprehensive Coverage
It covers essential libraries like Keras and PyTorch, ensuring that readers gain proficiency in popular frameworks while learning to identify and manage anomalies effectively.
Latest Techniques
Includes updated methodologies and advancements in deep learning, equipping readers with cutting-edge knowledge to tackle contemporary challenges in anomaly detection.
Product Details
| Publisher | Apress |
| Publication date | January 2, 2024 |
| Edition | Second |
| Language | English |
| Print length | 548 pages |
| ISBN-13 | 979-8868800078 |
| Item Weight | 2.07 pounds (940 grams) |
| Dimensions | 7.01 x 1.24 x 10 inches (17.8 x 3.1 x 25.4 cm) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists seeking to explore anomaly detection techniques using Python frameworks like Keras and PyTorch.
-
Machine Learning Enthusiasts
Perfect for learners wanting to understand anomaly detection concepts in deep learning through practical Python implementations.
-
AI Researchers
A good resource for researchers exploring new methodologies in anomaly detection within artificial intelligence and machine learning.
-
Absolute Beginners
Not suitable for individuals with no prior programming or machine learning knowledge, as it requires foundational skills.
Product Description
Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch Second Edition
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Pros
- Comprehensive learning material
- Easy-to-follow tutorials
- Practical examples included
- Supports popular frameworks
- Suitable for beginners and experts
Cons
- Some topics could be more detailed.
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INR 5440
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