课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

【解释】

The dimension of word vectors is usually smaller than the size of the vocabulary. Most common sizes for word vectors ranges between 50 and 400.

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

【解释】

过用t-SNE算法来将单词可视化。t-SNE算法所做的就是把这些n维的数据用一种非线性的方式映射到2维平面上,可以得知t-SNE中这种映射很复杂而且很非线性。

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP【解释】

Yes, word vectors empower your model with an incredible ability to generalize. The vector for "ecstatic would contain a positive/happy connotation which will probably make your model classified the sentence as a "1".

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

【解释】

Yes, the element-wise multiplication will be extremely inefficient.

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

【解释】

在skip-gram模型中,我们要做的是抽取上下文和目标词配对,来构造一个监督学习问题。上下文不一定总是目标单词之前离得最近的四个单词,或最近的n个单词。我们要的做的是随机选一个词作为上下文词,比如选orange这个词,然后我们要做的是随机在一定词距内选另一个词,比如在上下文词前后5个词内或者前后10个词内,我们就在这个范围内选择目标词。

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

课程五(Sequence Models),第二 周(Natural Language Processing & Word Embeddings) —— 0.Practice questions:Natural Language Processing & Word Embeddings-LMLPHP

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