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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/classification-vector-spaces-in-nlp
课程评论：没有评论
课程名称：自然语言处理与分类及向量空间 课程概述：在自然语言处理专业化的第一个课程中，您将： a) 使用逻辑回归和朴素贝叶斯法对推文进行情感分析， b) 利用向量空间模型发现单词之间的关系，并使用主成分分析（PCA）降低向量空间的维度，以可视化这些关系， c) 编写一个简单的英法翻译算法，使用预计算的词嵌入和局部敏感哈希，通过近似的 k 最近邻搜索来关联单词。 到本专业化结束时，您将能够设计自然语言处理应用程序，进行问答和情感分析，创建语言翻译和文本摘要工具，甚至构建一个聊天机器人！ 本专业化课程由两位在自然语言处理、机器学习和深度学习领域的专家设计和授课。Younes Bensouda Mourri是斯坦福大学的人工智能讲师，曾参与深度学习专业化的构建。Łukasz Kaiser是Google Brain的高级研究科学家，是TensorFlow、Tensor2Tensor和Trax库的共同作者，以及Transformer论文的合著者。 课程大纲： 第1部分：使用逻辑回归进行情感分析 描述：学习如何从文本中提取特征并转换为数值向量，然后构建一个用于推文的二元分类器，采用逻辑回归。 第2部分：使用朴素贝叶斯进行情感分析 描述：了解贝叶斯定理及条件概率的理论，然后应用于构建您自己的朴素贝叶斯推文分类器！ 第3部分：向量空间模型 描述：向量空间模型捕捉单词之间的语义含义和关系。您将学习如何创建捕捉单词依赖关系的词向量，并使用PCA在二维中可视化它们的关系。 第4部分：机器翻译与文档搜索 描述：学习如何转换词向量，并使用局部敏感哈希将其分配到子集，从而进行机器翻译和文档搜索。
Part: 1
Title:Sentiment Analysis with Logistic Regression
Description:Learn to extract features from text into numerical vectors, then build a binary classifier for tweets using a logistic regression!
Part: 2
Title:Sentiment Analysis with Naïve Bayes
Description:Learn the theory behind Bayes' rule for conditional probabilities, then apply it toward building a Naive Bayes tweet classifier of your own!
Part: 3
Title:Vector Space Models
Description:Vector space models capture semantic meaning and relationships between words. You'll learn how to create word vectors that capture dependencies between words, then visualize their relationships in two dimensions using PCA.
Part: 4
Title:Machine Translation and Document Search
Description:Learn to transform word vectors and assign them to subsets using locality sensitive hashing, in order to perform machine translation and document search.
In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search. 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.
