Adversarial Machine Learning with CSV and Image Data

سرفصل های دوره

Mastering Adversarial Machine Learning: Insights into Attack Techniques, Defense Strategies, and Ethical Considerations

1 - Introduction to Adversarial Machine Learning

1 - Overview of AI Security Challenges

2 - Evolution and Impact of Adversarial Attacks

3 - Setting Up the Environment for AML Practices

2 - The Nature of Adversarial Attacks

4 - Types and Techniques of Adversarial Attacks

5 - Practical Crafting Evasion Attacks on CSV FileTrained Models

6 - Practical Simulating Basic Adversarial Attacks on Image Models

3 - Developing Defense Mechanisms

7 - Overview of Defense Strategies against Adversarial Threats

8 - Practical Implementing Defenses for CSV FileTrained Models

9 - Practical Applying Defense Techniques to ImageTrained Models

4 - Advanced Adversarial Techniques

10 - Transferability of Adversarial Examples

11 - Generative Adversarial Networks GANs in AML

12 - Practical Creating and Defending Against Transferable Adversarial Examples

13 - Practical GAN Code for Adversarial Example Generation

5 - Case Studies and Ethical Considerations

14 - Analyzing RealWorld Adversarial Attacks in Different Industries

15 - Ethical Considerations in the Deployment of AML Strategies

16 - Practical Analyzing a RealWorld Case and Proposing a Defense Strategy

6 - Emerging Trends and Future Directions in Adversarial Machine Learning

17 - Adversarial Machine Learning in Quantum Computing

18 - AI Robustness in Edge Computing and ResourceConstrained Environments

19 - Adversarial Attacks and Defense in ZeroShot Learning

20 - Adversarial Attacks and Defense in Reinforcement Learning

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تولید کننده: Udemy