Course Code: slmsdsa
Duration: 28 hours
Prerequisites:
  • 對機器學習概念的基本瞭解
  • 熟悉 Python 程式設計
  • 自然語言處理基礎知識

觀眾

  • 數據科學家
  • 機器學習工程師
Overview:

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。

這種以講師為主導的現場培訓（在線或遠端）面向中級數據科學家和機器學習工程師，他們希望創建和應用為特定領域（如法律、醫學和技術領域）量身定製的小型語言模型。

在培訓結束時，參與者將能夠：

  • 瞭解特定領域語言模型的重要性和應用。
  • 整理和預處理用於模型訓練的專用數據集。
  • 為特定於領域的應用程式訓練和微調語言模型。
  • 使用領域相關指標對模型進行評估和基準測試。
  • 在實際場景中部署特定於領域的語言模型。

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。
Course Outline:

領域特定語言模型簡介

  • AI 中的語言模型概述
  • 語言模型專業化的重要性
  • 成功領域特定模型的案例研究

數據管理和預處理

  • 識別和收集特定領域的數據集
  • 數據清理和預處理技術
  • 數據集創建中的道德考量

模型訓練和微調

  • 遷移學習和微調簡介
  • 為特定領域的訓練選擇基本模型
  • 有效微調的技術

評估指標和模型性能

  • 特定領域模型評估的指標
  • 針對特定領域任務對模型進行基準測試
  • 瞭解局限性和權衡

部署策略

  • 將語言模型整合到特定領域的應用程式中
  • Scala部署模型的可及性和維護
  • 部署中的持續學習和模型更新

法律領域焦點

  • 法律語言模型的特殊注意事項
  • 培訓的判例法和成文法語料庫
  • 在法律研究和檔分析中的應用

醫療領域重點

  • 醫學語言處理的挑戰
  • HIPAA 合規性和數據隱私
  • 醫學文獻綜述和患者互動中的用例

技術領域重點

  • 技術術語及其對語言模型的影響
  • Collaboration 與主題專家
  • 技術文件生成和代碼註釋

專案與評估

  • 專案提案和初始數據集收集
  • 展示已完成的專案和模型性能
  • 最終評估和反饋

摘要和後續步驟

Sites Published:

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