- 瞭解基本的 AI 概念和術語
- 具有 Python 程式設計和數據分析經驗
- 熟悉深度學習框架，如 TensorFlow 或 PyTorch
觀眾
- 數據科學家
- AI 開發人員
- 人工智慧愛好者
生成式 AI （GenAI） 是一類 AI 演算法，可以從現有數據（例如圖像、文字、音訊等）創建新的逼真內容。生成式 AI 模型學習其輸入訓練數據的模式和結構，然後生成具有相似特徵的新數據。
這種以講師為主導的現場培訓（遠端或遠端）面向初級到中級的數據科學家、人工智慧開發人員和人工智慧愛好者，他們希望使用生成式人工智慧為各種目的創建新穎和多樣化的內容。
在培訓結束時，參與者將能夠：
- 設置包含生成式 AI 模型和工具的開發環境。
- 使用生成式 AI 從文字提示創建圖像、文字、音訊和其他內容。
- 在藝術、設計、娛樂和教育等不同領域使用生成式 AI。
- 評估生成式 AI 生成的內容的品質和多樣性。
- 瞭解生成式 AI 的倫理和社會影響。
課程形式
- 互動講座和討論。
- 大量的練習和練習。
- 在現場實驗室環境中動手實施。
課程定製選項
- 如需申請本課程的定製培訓，請聯繫我們安排。
介紹
- 什麼是生成式人工智慧？
- 生成式 AI 與其他類型的 AI
- 生成式人工智慧的主要技術和模型概述
- 生成式 AI 的應用和案例
- 生成式人工智慧的挑戰和局限性
使用生成式 AI 建立圖像
- 從文本描述生成圖像
- 使用 GAN 建立逼真和多樣化的圖像
- 使用 VAE 建立具有潛在變數的圖像
- 使用樣式傳遞將藝術樣式應用於圖像
使用生成式 AI 建立文字
- 從文本提示生成文字
- 使用基於 transformer 的模型創建具有上下文和連貫性的文本
- 使用文字摘要創建長文本的簡明摘要
- 使用文本釋義來創造表達相同含義的不同方式
使用生成式 AI 建立音訊
- 從文本生成語音
- 從語音生成文字
- 從文本或音訊生成音樂
- 使用特定語音生成語音
使用生成式 AI 建立其他內容
- 從自然語言生成代碼
- 從文本生成產品草圖
- 從文字或圖像生成視頻
- 從文字或圖像生成 3D 模型
評估生成式 AI
- 評估生成式 AI 中的內容品質和多樣性
- 使用初始分數、Fréchet 初始距離和 BLEU 分數等指標
- 通過眾包和調查利用人工評估
- 應用對抗性評估方法，如圖靈測試和判別器
瞭解生成式人工智慧的倫理和社會影響
- 確保公平和問責制
- 避免誤用和濫用
- 尊重內容創作者和消費者的權利和隱私
- 促進人類和人工智慧的創造力和協作
摘要和後續步驟
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