ISO/IEC 24029-1:2021互动点播简介培训课程：如何评估神经网络的稳健性Robustness for neural networks – ISO/IEC 24029-1:2021 introduction on-demand training course

等级专业人员时长 2.5 小时

可预订: 按需在线学习 ¥3700 点击报名

我们的AI培训课程旨在为个人和组织提供必要的知识和技能，以践行最佳实践标准，并在复杂的AI监管环境中探索。在这个快速发展的领域，了解AI的道德、法律和合规方面对于负责任和可持续的部署至关重要。

我们的课程涵盖了广泛的主题，从AI监管的基础知识到专门的行业特定考虑因素，为参与者提供了开发、部署和管理AI系统所需的工具。无论您是寻求提高专业知识的专业人士，还是旨在确保合规的组织，我们的培训计划都是为了满足您的需求，并在人工智能驱动的时代为您提供支持。

神经网络已经成为AI领域的一个强大工具，能够学习复杂的模式并做出准确的预测。然而，它们的稳健性一直是人们非常感兴趣和备受关注的话题。

在这里，我们深入探讨神经网络的稳健性概念，详细介绍其优点和局限性。我们探讨神经网络在遭遇对抗性攻击时面临的挑战，以及它们对扰动的敏感性，并介绍旨在增强其韧性的持续研究工作。通过了解神经网络的稳健性，我们可以为更可靠和值得信赖的AI系统铺平道路。

Assessing robustness of AI systems is a crucial part of their development and deployment. Here at BSI, we have developed a course that explores in depth the importance and impact of implementing robust deep learning systems, following the Standard ISO/IEC 24029-1:2021. We deep dive into the core of the standard, exploring all its technical parts and the corresponding actionable contents.

On-demand - training that’s even more flexible

BSI’s on-demand courses are market-leading and available 24/7. Developed by top subject matter experts, they contain the same high-quality content you will find in our tutor-led training, but with the added benefit of being able to learn at your own pace and at any time.

Who should attend?

数据科学家
数据分析师
数据工程师
机器学习工程师
人工智能架构师
Data Scientists
Data Analysts
Data Engineers
Machine Learning Engineers
AI architects

What will I learn?

Upon completion of this course, you will be able to:

Apply the methods provided by the standard to detect robustness issues arising in the development and deployment of a deep learning system
Describe the main principles behind data perturbation and abstract interpretation
Construct protocols to assess robustness of a neural network

On completion, you’ll be awarded an internationally recognized BSI training course certificate.
- 建立一套方法工具，可以帮助识别稳健性问题
- 了解不同类型的数据扰动及其在创建稳健性测试数据集中的应用
- 设计工作流程以检测和解决稳健性问题
- 采取措施确保稳健性评估成为涉及神经网络的人工智能系统开发和部署的一部分
- Build a toolset of methods that can help identifying robustness issues
- Understand the different types of data perturbations, and their use in the creation robustness test data sets
- Design workflows to detect and address robustness concerns
- Take steps to ensure that the assessment of robustness is part of the development and deployment of AI systems involving neural networks
This is an online, interactive on-demand course.

Courses are available 24/7 and you can learn at any time and from any place that suits you – you just need an internet connection.

You can learn as fast or as slowly as you want to. You can also take breaks at any time in the course and pick up where you left off when you are ready to continue

During the access period, you can go back and repeat parts or all the course

to refresh and reinforce what you have learned

The course content is both detailed and engaging, with explanations, activities, and knowledge checks to enhance your learning.
The aim of this course is to give you the technical tools to ensure that your deep learning models are robust, i.e., they perform well all circumstances, even in unforeseen and unusual situations.

We aim to give you knowledge of the main technical concepts of ISO/IEC 24029-1:2021, by explaining the different methods (statistical, formal, empirical) through which robustness can be assessed, and some more advanced concepts, such as data perturbation and abstract interpretation.

The course helps reinforce the importance of assessing robustness and understanding the impact that robustness issues can have on the development and deployment of deep learning systems

We aim to give you knowledge of the different contexts in which robustness concerns can arise, by providing examples of robustness concerns and give possible strategies for how to detect (and possibly address) such issues
The course assumes that delegates know the basic concepts of AI understand what a neural network is.

We recommend attending the: ‘How to assess robustness of neural networks awareness on-demand training course’

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