課程視頻：https://www.bilibili.com/video/BV1FT4y1E74V?p=151&;vd_source=d0416378a50b5f05a80e1ed2ccc0792f

對(duì)應(yīng)內(nèi)容：

Chapter 5: Sequence Models?

Week 1: Recurrent Neural Networks?

1.1 Why Sequence Models??

1.2 Notation?

1.3 Recurrent Neural Network Model?

1.4 Backpropagation through time?

1.5 Different types of RNNs?

1.6 Language model and sequence generation?

1.7 Sampling novel sequences?

1.8 Vanishing gradients with RNNs?

1.9 Gated Recurrent Unit GRU??

1.10 LSTM long short term memory unit?

1.11 Bidirectional RNN?

1.12 Deep RNNs?

Week 2: Natural Language Processing and Word Embeddings?

2.1 Word Representation?

2.2 Using Word Embeddings?

2.3 Properties of Word Embeddings?

2.4 Embedding Matrix?

2.5 Learning Word Embeddings?

2.6 Word2Vec

2.7 Negative Sampling?

2.8 GloVe Word Vectors?

2.9 Sentiment Classification?

2.10 Debiasing Word Embeddings?

Week 3: Sequence models & Attention mechanism?

3.1 Basic Models?

3.2 Picking the most likely sentence?

3.3 Beam Search?

3.4 Refinements to Beam Search?

3.5 Error analysis in beam search?

3.6? Bleu Score? optional??

3.7 Attention Model Intuition?

3.8 Attention Model?

3.9 Speech recognition?

3.10 Trigger Word Detection?

筆記：