引言

We introduce Recurrent Neural Networks and how they are able to feed in a sequence and predict either a fixed target (categorical/numerical) or another sequence (sequence to sequence).

卷积神经网络模型用于垃圾信息检测

We create an RNN model to improve on our spam/ham SMS text predictions.

LSTM模型用于文本生成

We show how to implement a LSTM (Long Short Term Memory) RNN for Shakespeare language generation. (Word level vocabulary)

堆叠多层LSTM

We stack multiple LSTM layers to improve on our Shakespeare language generation. (Character level vocabulary)

创建段对段模型翻译 (Seq2Seq)

We show how to use TensorFlow's sequence-to-sequence models to train an English-German translation model.

训练Siamese相似度测量

Here, we implement a Siamese RNN to predict the similarity of addresses and use it for record matching. Using RNNs for record matching is very versatile, as we do not have a fixed set of target categories and can use the trained model to predict similarities across new addresses.