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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-ai-probabilistic-perspective/
课程评论：没有评论
课程名称：生成性人工智能：概率论视角 概述：本课程“掌握生成性人工智能：从概率论的视角”深入探讨生成性人工智能的理论基础，旨在为学习者提供坚实的数学基础。课程以概率论的基本概念为起点，确保参与者能直观理解生成建模中至关重要的概率概念。在此基础上，课程系统地介绍了多种生成性人工智能架构，详细解释其基本原理。参与者将探讨变分自编码器（VAEs）、基于评分的模型、基于能量的模型和归一化流等内容，均从概率的角度进行分析。此外，该课程还涵盖了自回归模型，例如推动现代大语言模型（LLMs）的变换器，以及革新了AI生成媒体的生成对抗网络（GANs）。 与许多侧重实践编码的课程不同，本项目强调理论严谨性，使学习者能够深入理解这些架构的数学基础，而不仅仅是它们的实现方式。完成课程后，学生将对不同生成模型的工作原理有深刻的概念理解，具备批判性分析和发展自身AI研究的能力。此外，课程将这些模型与其现实应用相连接，帮助学习者理解在现代AI进步和未来创新中的重要性。 总体而言，本课程是从概率视角出发对生成性人工智能的创新介绍。
This course, Mastering Generative AI: A Probability Theory Perspective, is a deep theoretical exploration of Generative AI, designed to provide learners with a solid mathematical foundation. The course begins with the fundamentals of probability theory, ensuring that participants develop an intuitive understanding of key probabilistic concepts essential for generative modeling.Building on this foundation, the course systematically introduces various Generative AI architectures, explaining how they function from first principles. Participants will explore Variational Autoencoders (VAEs), Score-Based Models, Energy-Based Models, and Normalizing Flows, all through a probabilistic lens. The course also covers Autoregressive Models such as Transformers, which power modern Large Language Models (LLMs), and Generative Adversarial Networks (GANs), which revolutionized AI-generated media.Unlike many hands-on coding courses, this program emphasizes theoretical rigor, enabling learners to grasp the mathematical underpinnings of these architectures rather than just their implementation. By the end of the course, students will have a deep conceptual understanding of how different generative models work behind the scenes, equipping them with the knowledge to critically analyze and develop their own AI research. Additionally, the course connects these models to their real-world applications, helping learners appreciate their significance in modern AI advancements and future innovations.Overall this course is a novel introduction to Gen AI from the probability point of view.
