Learning to learn by gradient descent by gradient descent
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This document discusses using gradient descent to learn update rules for optimization problems. It proposes learning the optimizer itself by treating it as a differentiable function with its own parameters. The goal is to learn update rules rather than using hand-designed rules. The method and results sections are missing details on how this is implemented and evaluated.
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- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
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- Basic understanding of AWS services and architecture
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- Experience using the AWS Management Console or AWS CLI.
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# Scenario Covered:
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- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
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- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
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- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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Data Driven Maintenance | UReason Webinar
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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- 3. Introduction Objective function : 미분가능한 함수 gradient descent 사용
- 4. Introduction • hand-designed update rules-> learned update rule • Optimizer: g • Own set of parameters: • Optimizee: f
- 5. Methods • Function : • Loss :
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