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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-ai-fundamentals-specialization/
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课程名称：生成性人工智能基础 课程概述：欢迎参加Uplatz的生成性人工智能基础课程。生成性人工智能（genAI）是一门强大而令人兴奋的人工智能领域，侧重于创造新内容，而不是仅仅分析或解释现有数据。它可以生成多种输出，如文本（诗歌、代码、剧本、音乐作品、电子邮件、信件等）、图像（逼真的肖像、风景、抽象艺术、3D模型等）、音频（各种风格的音乐、音效、语音等）及视频（逼真模拟、风格化动画等）。 生成性人工智能的工作原理：可以将生成性人工智能视为一位经过大量数据（文本、图像等）训练的创意艺术家。其训练使其能够学习数据中的模式和关系，发展捕捉这些模式的统计模型，并根据用户提供的提示或输入生成全新的创作，这些创作虽类似于训练数据，但并非简单的复制。 生成性人工智能的不同技术： - 生成对抗网络（GANs）：两个AI模型竞争，一个创造新内容，另一个试图区分真假数据，从而提高生成模型创建现实输出的能力。 - 变分自编码器（VAEs）：将数据编码到潜在空间中，允许在该空间内进行操作和生成新数据点。 - 转换器（Transformers）：强大的神经网络架构，善于理解和生成文本、代码及其它序列数据。 关键要点： - 生成性人工智能仍在发展中，但其潜力正在迅速提升。 - 尽管高度创造性，但仍须记住它是机器，其输出的伦理影响需要谨慎考虑。 - 这是一个在艺术、设计、药物发现等多个领域具有强大应用工具的领域。 课程内容： 1. 生成性人工智能简介 - 什么是生成性人工智能？ - 生成性人工智能的发展历程 - 生成性人工智能的工作原理 - 生成性人工智能在不同领域的应用 2. 大型语言模型（LLM）简介 - 什么是大型语言模型？ - 大型语言模型如何工作？ - 大型语言模型的一般架构 - 语言模型的功能 - LLM的挑战与局限性 - LLM在人工智能领域的地位 - LLM的应用案例 3. 生成性人工智能：提示工程基础 - 什么是提示工程？ - 提示工程在生成性人工智能模型中的相关性 - 创建提示并探索影响力大的示例提示 - 常用的提示工程工具 4. 负责任的人工智能简介 - 什么是负责任的人工智能？ - 其重要性 - 谷歌如何在其产品中实施负责任的人工智能？ - 谷歌的七项人工智能原则 5. 生成性人工智能的影响、考虑和伦理问题 - 生成性人工智能的局限性和相关问题 - 确定与生成性人工智能相关的伦理问题、关注和误用情况 - 负责任使用生成性人工智能的考虑 - 生成性人工智能的经济和社会影响 通过本课程，学员将全面了解生成性人工智能的基础知识、技术以及在不同领域的应用与挑战。
A warm welcome to the Generative AI Fundamentals course by Uplatz.Generative AI, also known as genAI, is a powerful and exciting field of artificial intelligence focused on creating new content, unlike many other AI systems that primarily analyze or interpret existing data. It can produce diverse outputs like:Text: Poems, code, scripts, musical pieces, emails, letters, etc.Images: Photorealistic portraits, landscapes, abstract art, 3D models, etc.Audio: Music in various styles, sound effects, speech, etc.Video: Realistic simulations, stylized animations, etc.How Generative AI worksImagine generative AI as a highly creative artist trained on massive amounts of data (text, images, etc.). This training allows it to:Learn patterns and relationships within the data. For example, how words typically combine in sentences, how light interacts with objects to create an image, or how musical notes sequence to form melodies.Develop statistical models that capture these patterns. These models act like internal "recipes" for generating new content.Receive prompts or inputs from users, which guide the creative process. This could be a text description, a sketch, or even just a style preference.Use its models and the provided prompts to generate entirely new creations that resemble the training data but are not simply copies.Different techniques used in generative AIGenerative Adversarial Networks (GANs): Two AI models compete, one creating new content, the other trying to distinguish it from real data. This competition refines the generative model's ability to create realistic outputs.Variational Autoencoders (VAEs): Encode data into a latent space, allowing for manipulation and generation of new data points within that space.Transformers: Powerful neural network architectures adept at understanding and generating text, code, and other sequential data.Key points to rememberGenerative AI is still under development, but it's rapidly evolving with amazing potential.While highly creative, it's crucial to remember it's still a machine and the ethical implications of its outputs need careful consideration.It's a powerful tool for various applications like art, design, drug discovery, and more.Generative AI Fundamentals - Course CurriculumIntroduction to Generative AIWhat is Generative AI?Journey of Generative AIHow does Generative AI works?Applications of generative AI in different sectors and industriesIntroduction to Large Language Models (LLM)What is LLM?How do large language models work?General Architecture of Large Language ModelWhat can a language model do?What are the challenges and limitations of LLM?LLM in the AI landscapeLLM use cases/ApplicationGenerative AI: Prompt Engineering BasicsWhat is prompt Engineering?Relevance of prompt engineering in generative AI modelsCreating prompts and explore examples of impactful promptsCommonly used tools for prompt engineering to aid with prompt engineeringIntroduction to Responsible AIWhat is Responsible AI?Why it's important?How Google implements responsible AI in their products?Google's 7 AI principlesGenerative AI: Impact, Considerations, and Ethical IssuesLimitations of generative AI and the related concernsIdentify the ethical issues, concerns, and misuses associated with generative AIConsiderations for the responsible use of generative AIEconomic and social impact of generative AI
