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…
Autonomous driving - Car detection Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (…
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…
1. Neural Machine Translation 下面将构建一个神经机器翻译(NMT)模型,将人类可读日期 ("25th of June, 2009") 转换为机器可读日期 ("2009-06-25"). 使用 attention model. from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply from keras.layers…
第三周 序列模型和注意力机制(Sequence models & Attention mechanism) 3.1 序列结构的各种序列(Various sequence to sequence architectures) 首先,我们先建立一个网络,这个网络叫做编码网络(encoder network)(上图编号 1 所示),它是一个 RNN 的结构, RNN 的单元可以是 GRU 也可以是 LSTM.每次只向该网络中输入一个法语单词,将输入序列接收完毕后,这个 RNN 网络会输出一个向量来代表…
Optimization Welcome to the optimization's programming assignment of the hyper-parameters tuning specialization. There are many different optimization algorithms you could be using to get you to the minimal cost. Similarly, there are many different p…
参考 1. 基础模型(Basic Model) Sequence to sequence模型(Seq2Seq) 从机器翻译到语音识别方面都有着广泛的应用. 举例: 该机器翻译问题,可以使用"编码网络(encoder network)"+"解码网络(decoder network)"两个RNN模型组合的形式来解决. encoder network将输入语句编码为一个特征向量,传递给decoder network,完成翻译.具体模型结构如下图所示: 其中,encoder…
第三周 序列模型和注意力机制(Sequence models & Attention mechanism) 基础模型(Basic Models) 在这一周,你将会学习 seq2seq(sequence to sequence)模型,从机器翻译到语音识别,它们都能起到很大的作用,从最基本的模型开始.之后你还会学习集束搜索(Beam search)和注意力模型(Attention Model),一直到最后的音频模型,比如语音. 现在就开始吧,比如你想通过输入一个法语句子,比如这句 "Jane…
1. 基础模型 A. Sequence to sequence model:机器翻译.语音识别.(1. Sutskever et. al., 2014. Sequence to sequence learning with neural networks.   2. Cho et. al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation.) B…
20172325『Java程序设计』课程 结对编程练习_四则运算第三周阶段总结 结对伙伴 学号:20172306 姓名:刘辰 在这次项目的完成过程中刘辰同学付出了很多,在代码的实践上完成的很出色,在技术上提供了很多帮助.但是不足之处还是在于和结对伙伴沟通较少,使我不能准确的把握进程以及他的设计思路. 小组结对编程照片 小组成员感想 邓煜坤: (1)首先要说的是,在这个项目的完成过程中,我在节奏的掌控方面没有做的很好,导致时间过于紧张,有些部分没有顺利完成,没有做到最好. (2)在收获上面有较多的…