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所在平台: Udemy |
课程主页: https://www.udemy.com/course/decentralized-data-science/
课程评论：没有评论
**课程名称：** 去中心化数据科学 **课程概述：** 本课程并非传统的“数据科学”或“机器学习”课程，也**不包含任何编程内容**。本课程将探索前沿技术与去中心化方法在数据科学（尤其是在机器学习领域）的交叉应用及其变革潜力。 在ChatGPT引领AI竞赛的热潮下，各大科技巨头即将推出众多创新的AI模型。然而，在享受这场技术革新的同时，我们也应关注潜在的用户隐私风险。在AI竞赛中，科技巨头很可能利用用户数据来训练其模型，而中心化数据处理方式存在的各种安全漏洞，将使用户隐私面临威胁。 **去中心化数据科学**应运而生，它提供了一系列框架，如**联邦学习（Federated Learning）、差分隐私（Differential Privacy）、同态加密（Homomorphic Encryption）、安全多方计算（Secure Multi-Party Computations）和边缘计算（Edge Computing）**。这些框架能够实现在保护用户隐私的前提下对数据进行处理。 课程还将探讨**TensorFlow Federated**和**TensorFlow Lite**等工具，它们能够帮助我们构建这些去中心化的机器学习系统。 **核心内容：** * 去中心化数据科学的定义与重要性 * AI竞赛中的隐私挑战 * 联邦学习 * 差分隐私 * 同态加密 * 安全多方计算 * 边缘计算 * TensorFlow Federated 和 TensorFlow Lite 等工具的应用 本课程旨在帮助学习者理解如何在不牺牲用户隐私的前提下，利用去中心化技术推动数据科学和机器学习的创新。
Please note that this is not a Data Science or Machine Learning course. This course does not cover any coding. Welcome to the course on "Decentralized Data Science" - an exploration into the intersection of cutting-edge technologies and the transformative power of decentralized approaches in Data Science - especially in Machine Learning. ChatGPT brought us to the verge of an AI Race. It is expected that in the coming months and years, all the tech majors will launch many new AI models. We are all excited about the sector that is poised for dramatic innovation. But, is there anything we should be concerned about? Yes. Privacy.These tech majors are likely to use user data to train their models. As centralized data processing involves various vulnerabilities, user privacy will be at stake in this AI Race. So, is there any way to preserve user privacy in Machine Learning? This is where Decentralized Data Science comes in. Decentralized Machine Learning offers various frameworks such as Federated Learning, Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computations, and Edge Computing. These frameworks enable processing of data while preserving user privacy. We will also discuss tools such as TensorFlow Federated and TensorFlow Lite that help us build these decentralized machine learning systems. Let us discuss these concepts in this course
