- 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
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。
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
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