Mueller, John Paul
Machine Learning Security Principles: Keep data, networks, users, and applications safe from prying eyes
- ISBN 13:
- 9781804618851
- author:
- Mueller, John Paul
- format:
- Paperback
- publisher:
- Packt Publishing
- language:
- English
- Publication Year:
- 2022
- Pages:
- 450
- Dimensions:
- 23.5 x 19.1 x 2.3 centimetres (0
- Genre:
- Computers, Special Topics, Viruses,
- Condition:
- New
- Availability:
- Item usually sent within 5 working days
Description
Preventing and detecting hacking attempts is crucial to safeguarding data, networks, users, and applications from malicious access. This comprehensive guide explores the security principles essential for machine learning systems.
Discover how hackers exploit misdirection and deep fakes to bypass even the most robust security measures. Learn how to detect unwanted modifications to your data and develop application code that meets the necessary security requirements for machine learning.
This book delves into the world of machine learning, exploring its applications, common environments, and the security threats they pose. You'll learn about detecting hacker behaviors, mitigating deep fake attacks, and best practices for ethical data sourcing to reduce security risk.
Related Products
Related Products
Python Machine Learning for Beginners: Learning from scratch NumPy, Pandas, Matplotlib, Seaborn, Scikitlearn, and TensorFlow for Machine Learning and Data Science (Machine Learning & Data Science for Beginners)
Publishing, Ai
Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
Lantz, Brett
Keep You Safe
Hill, Melissa
Building Machine Learning Powered Applications: Going from Idea to Product
Ameisen, Emmanuel
I'll Keep You Safe
May, Peter