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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tensorflow-word2vec-word-embeddings/
课程评论：没有评论
课程名称：Word2Vec：使用TensorFlow构建语义推荐系统 课程概览：在这门Word2Vec教程中，您将学习如何训练一个Word2Vec Python模型，并基于一到两个给定的名称进行语义推荐。该教程旨在突出使用TensorFlow构建Word2Vec模型的有趣和实质性部分。Word2Vec是一组相关模型，用于生成词嵌入。与早期的潜在语义分析算法相比，使用Word2Vec算法创建的嵌入向量具有许多优势。词嵌入是文档词汇的最流行表示之一，能够捕捉文档中单词的语境、语义和句法相似性，以及与其他单词的关系等。词嵌入是特定单词的向量表示。本课程通过在Python库TensorFlow中实现算法来帮助您深入理解词嵌入的原理。 Word2Vec是使用浅层神经网络学习词嵌入的最流行技术之一，且其计算效率高，是从原始文本中学习词嵌入的有效预测模型。在本教程中，您将学习Word2Vec的基本思想：利用一个具有三层结构的神经网络（1个输入层 + 1个隐藏层 + 1个输出层），输入一个单词并训练其预测相邻的单词。随后，将移除输出层，仅保留输入层和隐藏层。通过该隐藏层的输出，我们可以获得输入单词的“词嵌入”。在本Word2Vec教程中，我们将完成构建和训练Word2Vec Python模型的所有步骤，包括预处理、标记化、批处理、构建Word2Vec模型，当然还有训练。最终，我们将利用训练好的Word2Vec Python模型，基于一到两个给定的名称进行语义推荐。让我们开始吧！
In this Word2Vec tutorial, you will learn how to train a Word2Vec Python model and use it to semantically suggest names based on one or even two given names.This Word2Vec tutorial is meant to highlight the interesting, substantive parts of building a word2vec Python model with TensorFlow.Word2vec is a group of related models that are used to produce Word Embeddings. Embedding vectors created using the Word2vec algorithm have many advantages compared to earlier algorithms such as latent semantic analysis.Word embedding is one of the most popular representation of document vocabulary. It is capable of capturing context of a word in a document, semantic and syntactic similarity, relation with other words, etc. Word Embeddings are vector representations of a particular word.The best way to understand an algorithm is to implement it. So, in this course you will learn Word Embeddings by implementing it in the Python library, TensorFlow.Word2Vec is one of the most popular techniques to learn word embeddings using shallow neural network. Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text.In this Word2Vec tutorial, you will learn The idea behind Word2Vec:Take a 3 layer neural network. (1 input layer + 1 hidden layer + 1 output layer)Feed it a word and train it to predict its neighbouring word.Remove the last (output layer) and keep the input and hidden layer.Now, input a word from within the vocabulary. The output given at the hidden layer is the ‘word embedding' of the input word.In this Word2Vec tutorial we are going to do all steps of building and training a Word2vec Python model (including pre-processing, tokenizing, batching, structuring the Word2Vec Python model and of course training it) using Python TensorFlow. Finally, we are going to use our trained Word2Vec Python model to semantically suggest names based on one or even two given names.Let's start!
