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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/sequence-models-in-nlp
课程评论：没有评论
课程名称：基于序列模型的自然语言处理 课程概述：在自然语言处理专项课程的第三部分中，您将学习： a) 使用GLoVe词嵌入训练神经网络，以进行推文的情感分析； b) 使用门控递归单元（GRU）语言模型生成合成的莎士比亚文本； c) 训练递归神经网络，使用具有线性层的LSTM进行命名实体识别（NER）； d) 使用所谓的“西洋棋”LSTM模型比较语料库中的问题，识别措辞不同但含义相同的问题。 在完成这一专项课程后，您将能够设计执行问答和情感分析的NLP应用，创建翻译语言和总结文本的工具，甚至构建聊天机器人！ 本课程由两位NLP、机器学习和深度学习领域的专家设计和教授。Younes Bensouda Mourri是斯坦福大学的人工智能讲师，参与了深度学习专项课程的构建。Łukasz Kaiser是谷歌大脑的研究科学家，也是Tensorflow、Tensor2Tensor和Trax库的共同作者，以及Transformer论文的作者。 课程大纲： 第一部分：情感分析的神经网络 描述：学习深度学习中的神经网络，构建一个复杂的推文分类器，将推文归类为正面或负面的情感类别。 第二部分：用于语言建模的递归神经网络 描述：了解传统语言模型的局限性，掌握如何使用RNN和GRU处理顺序数据进行文本预测。然后使用莎士比亚文本数据构建自己的下一个单词生成器！ 第三部分：LSTM与命名实体识别 描述：学习长短期记忆单元（LSTMs）如何解决梯度消失问题，以及命名实体识别系统如何快速从文本中提取重要信息。然后使用LSTM和Kaggle的数据构建自己的命名实体识别系统！ 第四部分：西洋棋网络 描述：了解西洋棋网络，这是一种由两个相同网络组成的特殊神经网络，最后合并在一起。然后构建自己的西洋棋网络，以识别Quora数据集中的问题重复项。
Part: 1
Title:Neural Networks for Sentiment Analysis
Description:Learn about neural networks for deep learning, then build a sophisticated tweet classifier that places tweets into positive or negative sentiment categories, using a deep neural network.
Part: 2
Title:Recurrent Neural Networks for Language Modeling
Description:Learn about the limitations of traditional language models and see how RNNs and GRUs use sequential data for text prediction. Then build your own next-word generator using a simple RNN on Shakespeare text data!
Part: 3
Title:LSTMs and Named Entity Recognition
Description:Learn about how long short-term memory units (LSTMs) solve the vanishing gradient problem, and how Named Entity Recognition systems quickly extract important information from text. Then build your own Named Entity Recognition system using an LSTM and data from Kaggle!
Part: 4
Title:Siamese Networks
Description:Learn about Siamese networks, a special type of neural network made of two identical networks that are eventually merged together, then build your own Siamese network that identifies question duplicates in a dataset from Quora.
In Course 3 of the Natural Language Processing Specialization, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
