Operations on word vectors Welcome to your first assignment of this week! Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings. After this assignment you will be able to: L…

Operations on word vectors Welcome to your first assignment of this week! Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings. After this assignment you will be able to: L…

第二周 自然语言处理与词嵌入(Natural Language Processing and Word Embeddings) 词汇表征(Word Representation) 上周我们学习了 RNN.GRU 单元和 LSTM 单元.本周你会看到我们如何把这些知识用到 NLP 上,用于自然语言处理,深度学习已经给这一领域带来了革命性的变革.其中一个很关键的概念就是词嵌入(word embeddings),这是语言表示的一种方式,可以让算法自动的理解一些类似的词,比如男人对女人,比如国王对王后,…

Word embeding 给word 加feature,用来区分word 之间的不同,或者识别word之间的相似性. 用于学习 Embeding matrix E 的数据集非常大,比如 1B - 100B 的word corpos. 所以即使你输入的是没见过的 durian cutivator 也知道和 orange farmer 很相近. 这是transfter learning 的一个case. 因为t-SNE 做了non-liner 的转化,所以在原来的300维空间的平行的向量在转化过后…

Week 2 Quiz: Natural Language Processing and Word Embeddings (第二周测验:自然语言处理与词嵌入) 1.Suppose you learn a word embedding for a vocabulary of 10000 words. Then the embedding vectors should be 10000 dimensional, so as to capture the full range of variation…

[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. [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. [解释] Yes, word v…

2 Natural Language Processing & Word Embeddings 2.1 Word Representation(单词表达) vocabulary,每个单词可以使用1-hot表示,写作\(O^{5391}\)之类,上标可以变.只是用1-hot,不能知道任意两个单词的关系,例如man/woman;king/queen;apple/orange. 特征化表示:词嵌入(Featurized representation:word embedding).一个特征,使用-1到…

Sequence Models This is the fifth and final course of the deep learning specialization at Coursera which is moderated by deeplearning.ai Here are the course summary as its given on the course link: This course will teach you how to build models for n…

About this Course This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting…

Sequence to Sequence models basic sequence-to-sequence model: basic image-to-sequence or called image captioning model: but there are some differences between how you write a model like this to generate a sequence, compared to how you were synthesizi…