Course Code: dlnlu
Duration: 21 hours
Prerequisites:
  • 自然語言處理 (NLP) 的高級知識
  • 深度學習框架使用經驗
  • 熟悉神經網路架構

觀眾

  • 數據科學家
  • AI 研究人員
  • 機器學習工程師
Overview:

本課程重點介紹專為 Natural Language Understanding (NLU) 量身定製的高級深度學習架構,探索 NLU 與傳統 NLP 模型有何不同。參與者將獲得構建用於語義理解的深度學習模型的實踐經驗,並探索語言理解的未來趨勢。

這種由講師指導的現場培訓(在線或現場)面向希望專注於 NLU 尖端深度學習技術的高級專業人士。

在本次培訓結束時,參與者將能夠:

  • 瞭解 NLU 和 NLP 模型之間的主要區別。
  • 將高級深度學習技術應用於 NLU 任務。
  • 探索 Transformer 和 Attention 機制等深層架構。
  • 利用 NLU 的未來趨勢來構建複雜的 AI 系統。

課程形式

  • 互動講座和討論。
  • 大量的練習和練習。
  • 在即時實驗室環境中動手實施。

課程自定義選項

  • 要申請本課程的定製培訓,請聯繫我們進行安排。
Course Outline:

NLU 的 Deep Learning 簡介

  • NLU 與 NLP 概述
  • 自然語言處理中的深度學習
  • 特定於 NLU 模型的挑戰

適用於 NLU 的深度架構

  • 變壓器和注意力機制
  • 用於語義解析的遞迴神經網路 (RNN)
  • 預訓練模型及其在 NLU 中的作用

語義理解和 Deep Learning

  • 構建用於語義分析的模型
  • NLU 的上下文嵌入
  • 語義相似性和蘊涵任務

NLU 中的高級技術

  • 用於理解上下文的序列到序列模型
  • 用於意圖識別的深度學習
  • NLU 中的遷移學習

評估深度 NLU 模型

  • 用於評估 NLU 性能的指標
  • 處理深度 NLU 模型中的偏差和錯誤
  • 提高 NLU 系統的可解釋性

ScalaNLU 系統的可用性和優化

  • 為大規模 NLU 任務優化模型
  • 高效利用計算資源
  • 模型壓縮和量化

NLU 的 Deep Learning 中的未來趨勢

  • 轉換器和語言模型的創新
  • 探索多模態 NLU
  • 超越 NLP:上下文和語義驅動的 AI

總結和後續步驟

Sites Published:

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