Course Code: dlnlu
Duration: 21 hours
Prerequisites:
  • 自然语言处理 （NLP） 的高级知识
  • 深度学习框架使用经验
  • 熟悉神经网路架构

观众

  • 数据科学家
  • AI 研究人员
  • 机器学习工程师
Overview:

本课程重点介绍专为 Natural Language Understanding (NLU) 量身定制的高级深度学习架构，探索 NLU 与传统 NLP 模型有何不同。参与者将获得构建用于语义理解的深度学习模型的实践经验，并探索语言理解的未来趋势。

这种由讲师指导的现场培训（在线或现场）面向希望专注于 NLU 尖端深度学习技术的高级专业人士。

在本次培训结束时，参与者将能够：

  • 了解 NLU 和 NLP 模型之间的主要区别。
  • 将高级深度学习技术应用于 NLU 任务。
  • 探索 Transformer 和 Attention 机制等深层架构。
  • 利用 NLU 的未来趋势来构建复杂的 AI 系统。

课程形式

  • 互动讲座和讨论。
  • 大量的练习和练习。
  • 在即时实验室环境中动手实施。

课程自定义选项

  • 要申请本课程的定制培训，请联系我们进行安排。
Course Outline:

NLU 的 Deep Learning 简介

  • NLU 与 NLP 概述
  • 自然语言处理中的深度学习
  • 特定于 NLU 模型的挑战

适用于 NLU 的深度架构

  • 变压器和注意力机制
  • 用于语义解析的递回神经网路 （RNN）
  • 预训练模型及其在 NLU 中的作用

语义理解和 Deep Learning

  • 构建用于语义分析的模型
  • NLU 的上下文嵌入
  • 语义相似性和蕴涵任务

NLU 中的高级技术

  • 用于理解上下文的序列到序列模型
  • 用于意图识别的深度学习
  • NLU 中的迁移学习

评估深度 NLU 模型

  • 用于评估 NLU 性能的指标
  • 处理深度 NLU 模型中的偏差和错误
  • 提高 NLU 系统的可解释性

ScalaNLU 系统的可用性和优化

  • 为大规模 NLU 任务优化模型
  • 高效利用计算资源
  • 模型压缩和量化

NLU 的 Deep Learning 中的未来趋势

  • 转换器和语言模型的创新
  • 探索多模态 NLU
  • 超越 NLP：上下文和语义驱动的 AI

总结和后续步骤

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