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所在平台: Udemy |
课程主页: https://www.udemy.com/course/nlp-with-transformers/
课程评论：没有评论
课程名称：Python中的自然语言处理：使用变换器 课程概述：在现代自然语言处理（NLP）领域，变换器模型已成为标准。它们作为最具表现力和强大的语言模型，远远超越了所有重要的基于语言的基准。在本课程中，我们将学习如何使用变换器模型（如谷歌AI的BERT和Facebook AI的DPR）构建尖端的NLP应用程序。 课程内容涵盖以下多个关键NLP框架： - HuggingFace的Transformers - TensorFlow 2 - PyTorch - spaCy - NLTK - Flair 我们将学习将变换器应用于流行的NLP用例，包括： - 语言分类/情感分析 - 命名实体识别（NER） - 问答系统 - 相似性/比较学习 在这些用例的讲解中，我们将通过多种示例深入理解变换器为何如此重要。此外，课程还包括两个完整的NLP项目，一个分析金融Reddit数据的情感分析项目，另一个是一个全面的开放领域问答应用。 课程还将涵盖多个支持学习的主题，包括： - NLP的发展历史以及变换器的起源 - 常见的NLP预处理技术 - 变换器的理论基础 - 如何微调变换器 我们将学习这些内容及更多，期待在课程中与您见面！
Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.We cover several key NLP frameworks including:HuggingFace's TransformersTensorFlow 2PyTorchspaCyNLTKFlairAnd learn how to apply transformers to some of the most popular NLP use-cases:Language classification/sentiment analysisNamed entity recognition (NER)Question and AnsweringSimilarity/comparative learningThroughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:History of NLP and where transformers come fromCommon preprocessing techniques for NLPThe theory behind transformersHow to fine-tune transformersWe cover all this and more, I look forward to seeing you in the course!
