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		<title>Word2vec with TensorFlow 2.0 &#8211; a simple CBOW implementation</title>
		<link>https://petaminds.com/word2vec-with-tensorflow-2-0-a-simple-cbow-implementation/</link>
					<comments>https://petaminds.com/word2vec-with-tensorflow-2-0-a-simple-cbow-implementation/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sat, 19 Oct 2019 23:30:20 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[CBOW]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[natural language processing]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[tenso]]></category>
		<category><![CDATA[word2vec]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1144</guid>

					<description><![CDATA[<p>In TensorFlow website, there is a good example of word embedding implementation with Keras. Nevertheless, we are curious to see how it looks like when implementing word2vec with PURE TensorFlow 2.0. What is CBOW In the previous article, we introduced Word2vec (w2v) with Gensim library. Word2vec consists of two-layer neural networks that are trained to reconstruct linguistic [&#8230;]</p>
<p>The post <a href="https://petaminds.com/word2vec-with-tensorflow-2-0-a-simple-cbow-implementation/">Word2vec with TensorFlow 2.0 &#8211; a simple CBOW implementation</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
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