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所在平台: Udemy |
课程主页: https://www.udemy.com/course/xai-with-python/
课程评论：没有评论
课程名称：使用Python进行可解释人工智能（XAI） 课程概述： 本课程深入探讨了可解释人工智能（XAI）的最新发展。随着我们对人工智能模型依赖的日益增加，了解AI做出特定决策的原因与过程变得同样重要。近期的法律法规也加速了对AI系统决策进行解释和辩护的需求。本课程旨在使用Python讨论可视化、解释和构建可靠AI系统的工具和技术。 课程内容涵盖LIME（局部可解释模型无关解释）和SHAP（夏普利增益解释）的工作原理及数学建模，帮助生成局部和全球解释。此外，课程还讨论了反事实和对比解释的必要性，以及多样化反事实解释（DiCE）等技术的原理和应用，旨在生成可操作的反事实解释。课程中也涵盖了AI公平性和通过谷歌的What-If工具（WIT）生成可视化解释的概念，以及用于神经网络解释的层级相关传播（LRP）技术。 学员将在本课程中学习如何使用Python的各种工具和技术来可视化、解释并构建值得信赖的AI系统。课程通过多个案例研究强调了解释技术在关键应用领域的重要性，并通过实践环节清晰讲解各项技术，以帮助学员掌握代码并自信地应用于自己的AI模型。此外，课程还提供用于实施各种XAI技术的数据集和代码，以供学员练习。
XAI with PythonThis course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This course discusses tools and techniques using Python to visualize, explain, and build trustworthy AI systems. This course covers the working principle and mathematical modeling of LIME (Local Interpretable Model Agnostic Explanations), SHAP (SHapley Additive exPlanations) for generating local and global explanations. It discusses the need for counterfactual and contrastive explanations, the working principle, and mathematical modeling of various techniques like Diverse Counterfactual Explanations (DiCE) for generating actionable counterfactuals. The concept of AI fairness and generating visual explanations are covered through Google's What-If Tool (WIT). This course covers the LRP (Layer-wise Relevance Propagation) technique for generating explanations for neural networks.In this course, you will learn about tools and techniques using Python to visualize, explain, and build trustworthy AI systems. The course covers various case studies to emphasize the importance of explainable techniques in critical application domains.All the techniques are explained through hands-on sessions so that learns can clearly understand the code and can apply it comfortably to their AI models. The dataset and code used in implementing various XAI techniques are provided to the learners for their practice.
