原文链接:https://colah.github.io/posts/2015-08-Understanding-LSTMs/ Understanding LSTM Networks Recurrent Neural Networks Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understa…

LSTM’s in Pytorch Example: An LSTM for Part-of-Speech Tagging Exercise: Augmenting the LSTM part-of-speech tagger with character-level features Sequence models are central to NLP: they are models where there is some sort of dependence through time be…

长时依赖是这样的一个问题,当预测点与依赖的相关信息距离比较远的时候,就难以学到该相关信息.例如在句子”我出生在法国,……,我会说法语“中,若要预测末尾”法语“,我们需要用到上下文”法国“.理论上,递归神经网络是可以处理这样的问题的,但是实际上,常规的递归神经网络并不能很好地解决长时依赖,好的是LSTMs可以很好地解决这个问题. 原理不再介绍. keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoi…

第一周 循环序列模型(Recurrent Neural Networks) 为什么选择序列模型?(Why Sequence Models?) 在本课程中你将学会序列模型,它是深度学习中最令人激动的内容之一.循环神经网络(RNN)之类的模型在语音识别.自然语言处理和其他领域中引起变革.在本节课中,你将学会如何自行创建这些模型.我们先看一些例子,这些例子都有效使用了序列模型. 在进行语音识别时,给定了一个输入音频片段 \(X\),并要求输出对应的文字记录 \(Y\).这个例子里输入和输出数据都是序列…

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…

1 Recurrent Neural Networks(循环神经网络) 1.1 序列数据 输入或输出其中一个或两个是序列构成.例如语音识别,自然语言处理,音乐生成,感觉分类,dna序列,机器翻译,视频状态识别,名称识别. 1.2 Notation(符号) \(x ^ { ( i ) < t > }\)表示第\(i\)个训练样本输入的第\(t\)个元素 \(T ^ { ( i ) < t > } _ x\)表示第\(i\)个训练样本输入的长度为\(t\) \(y ^ { ( i )…

Lesson 5 Sequence Models 这篇文章其实是 Coursera 上吴恩达老师的深度学习专业课程的第五门课程的课程笔记. 参考了其他人的笔记继续归纳的. 符号定义 假如我们想要建立一个能够自动识别句中人名地名等位置的序列模型,也就是一个命名实体识别问题,这常用于搜索引擎.命名实体识别系统可以用来查找不同类型的文本中的人名.公司名.时间.地点.国家名和货币名等等. 我们输入语句 "Harry Potter and Herminoe Granger invented a new s…

第一周 循环序列模型(Recurrent Neural Networks) 1.1 为什么选择序列模型?(Why Sequence Models?) 1.2 数学符号(Notation) 这个输入数据是 9 个单词组成的序列,所以会有 9 个特征集和来表示这 9 个 单词,并按序列中的位置进行索引,用\(…

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…