【课程五(Sequence Models)，第二 周（Natural Language Processing & Word Embeddings） —— 2.Programming assignments：Emojify】的更多相关文章

吴恩达《深度学习》-课后测验-第五门课 序列模型(Sequence Models)-Week 2: Natural Language Processing and Word Embeddings (第二周测验：自然语言处理与词嵌入)

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…

课程五(Sequence Models)，第二 周（Natural Language Processing & Word Embeddings） —— 2.Programming assignments：Emojify

Emojify! Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier. Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that. So rather than wri…

课程五(Sequence Models)，第二 周（Natural Language Processing & Word Embeddings） —— 1.Programming assignments：Operations on word vectors - Debiasing

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…

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

[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…

Coursera, Deep Learning 5, Sequence Models, week2, Natural Language Processing & Word Embeddings

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…

吴恩达《深度学习》-第五门课 序列模型(Sequence Models)-第二周 自然语言处理与词嵌入（Natural Language Processing and Word Embeddings）-课程笔记

第二周 自然语言处理与词嵌入(Natural Language Processing and Word Embeddings) 2.1 词汇表征(Word Representation) 词汇表示,目前为止一直都是用词汇表来表示词,上周提到的词汇表,可能是 10000 个单词,我们一直用 one-hot 向量来表示词.这种表示方法的一大缺点就是它把每个词孤立起来,这样使得算法对相关词的泛化能力不强. 换一种表示方式会更好,如果不用 one-hot 表示,而是用特征化的表示来表示每个词,man,w…

课程五(Sequence Models)，第一 周（Recurrent Neural Networks） —— 1.Programming assignments：Building a recurrent neural network - step by step

Building your Recurrent Neural Network - Step by Step Welcome to Course 5's first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy. Recurrent Neural Networks (RNN) are very effective for Natural Language…

课程五(Sequence Models)，第三周（Sequence models & Attention mechanism） —— 1.Programming assignments：Neural Machine Translation with Attention

Neural Machine Translation Welcome to your first programming assignment for this week! You will build a Neural Machine Translation (NMT) model to translate human readable dates ("25th of June, 2009") into machine readable dates ("2009-06-25…

课程五(Sequence Models)，第一 周（Recurrent Neural Networks） —— 2.Programming assignments：Dinosaur Island - Character-Level Language Modeling

Character level language model - Dinosaurus land Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of…

课程五(Sequence Models)，第三周（Sequence models & Attention mechanism） —— 2.Programming assignments：Trigger word detection

Expected OutputTrigger Word Detection Welcome to the final programming assignment of this specialization! In this week's videos, you learned about applying deep learning to speech recognition. In this assignment, you will construct a speech dataset a…