- 了解基本的 AI 概念和术语
- 具有 Python 编程和数据分析经验
- 熟悉深度学习框架，如 TensorFlow 或 PyTorch
观众
- 数据科学家
- AI 开发人员
- 人工智能爱好者
生成式 AI （GenAI） 是一类 AI 算法，可以从现有数据（例如图像、文本、音频等）创建新的逼真内容。生成式 AI 模型学习其输入训练数据的模式和结构，然后生成具有相似特征的新数据。
这种以讲师为主导的现场培训（远程或远程）面向初级到中级的数据科学家、人工智能开发人员和人工智能爱好者，他们希望使用生成式人工智能为各种目的创建新颖和多样化的内容。
在培训结束时，参与者将能够：
- 设置包含生成式 AI 模型和工具的开发环境。
- 使用生成式 AI 从文本提示创建图像、文本、音频和其他内容。
- 在艺术、设计、娱乐和教育等不同领域使用生成式 AI。
- 评估生成式 AI 生成的内容的质量和多样性。
- 了解生成式 AI 的伦理和社会影响。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在现场实验室环境中动手实施。
课程定制选项
- 如需申请本课程的定制培训，请联系我们安排。
介绍
- 什么是生成式人工智能？
- 生成式 AI 与其他类型的 AI
- 生成式人工智能的主要技术和模型概述
- 生成式 AI 的应用和用例
- 生成式人工智能的挑战和局限性
使用生成式 AI 创建图像
- 从文本描述生成图像
- 使用 GAN 创建逼真和多样化的图像
- 使用 VAE 创建具有潜在变量的图像
- 使用样式传递将艺术样式应用于图像
使用生成式 AI 创建文本
- 从文本提示生成文本
- 使用基于 transformer 的模型创建具有上下文和连贯性的文本
- 使用文本摘要创建长文本的简明摘要
- 使用文本释义来创造表达相同含义的不同方式
使用生成式 AI 创建音频
- 从文本生成语音
- 从语音生成文本
- 从文本或音频生成音乐
- 使用特定语音生成语音
使用生成式 AI 创建其他内容
- 从自然语言生成代码
- 从文本生成产品草图
- 从文本或图像生成视频
- 从文本或图像生成 3D 模型
评估生成式 AI
- 评估生成式 AI 中的内容质量和多样性
- 使用初始分数、Fréchet 初始距离和 BLEU 分数等指标
- 通过众包和调查利用人工评估
- 应用对抗性评估方法，如图灵测试和判别器
了解生成式人工智能的伦理和社会影响
- 确保公平和问责制
- 避免误用和滥用
- 尊重内容创作者和消费者的权利和隐私
- 促进人类和人工智能的创造力和协作
摘要和后续步骤
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