- 對機器學習概念的基本瞭解
- 熟悉 Python 程式設計
- 神經網路和深度學習知識
觀眾
- 數據科學家
- 軟體開發人員
- AI愛好者
小型語言模型 （SLM） 是 AI 的一個尖端子集，可在計算資源有限的設備上實現高效的語言處理。
這種由講師指導的現場培訓（在線或遠端）面向希望在各種應用程式中實施和利用小型語言模型的初級到中級數據科學家和開發人員。
在培訓結束時，參與者將能夠：
- 瞭解小型語言模型的體系結構和功能。
- 為文本生成和情緒分析等任務實施 SLM。
- 針對特定用例優化和微調 SLM。
- 在資源受限的環境中部署 SLM。
- 評估和解釋 SLM 在實際場景中的性能。
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。
課程自定義選項
- 如需申請本課程的定製培訓，請聯繫我們進行安排。
小型語言模型 （SLM） 簡介
- 語言模型概述
- 從大型語言模型到小型語言模型的演變
- SLM的體系結構和設計
- SLM的優點和局限性
技術基礎
- 了解神經網路和參數
- SLM的培訓過程
- 數據需求和模型優化
- 語言模型的評估指標
自然語言處理中的 SLM
- 使用 SLM 產生文字
- 語言翻譯和當地語系化
- 情感分析和文本分類
- 問答和聊天機器人
SLM 的實際應用
- 移動應用程式：設備端語言處理
- 嵌入式系統：物聯網設備中的 SLM
- 隱私保護 AI：本地數據處理
- 邊緣計算：低延遲環境中的 SLM
案例研究
- 分析SLM的成功部署
- 行業特定應用（醫療保健、Finance 等）
- 比較研究：SLM 與生產中的大型模型
未來方向
- SLM的研究趨勢
- 擴展和部署方面的挑戰
- 道德考量和負責任的人工智慧
- 未來之路：下一代SLM
實踐研討會
- 構建用於文字生成的簡單 SLM
- 將 SLM 整合到行動應用程式中
- 針對特定任務微調 SLM
- 性能分析和模型可解釋性
頂點專案
- 確定 SLM 應用程式的問題空間
- 設計和實施SLM解決方案
- 測試和反覆運算模型
- 介紹專案和成果
摘要和後續步驟
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