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RNN W3L09 Speech Recognition

RNN 1 Recurrent Neural Networks — Introduction | Motivation | Why Sequence Models

RNN 2 Notation | Sentence and Word Representation | Sequence Models

RNN 3 Recurrent Neural Network Model | RNN Model

RNN 4 Backpropagation through time | BPTT | RNN

RNN W1L05 Different types of RNNs

RNN W1L06 Language Model and sequence generation

RNN W1L07 Sampling novel Sequences

RNN W1L08 Vanishing gradients with RNNs

RNN W1L09 Gated Recurrent Unit GRU

RNN W1L10 Long Short Term Memory LSTM

LSTM in Keras | Understanding LSTM input and output shapes

RNN W1L11 Bidirectional RNN

RNN W1L12 Deep RNNs

RNN W2L01 Word Representation

RNN W2L02 Using Word Embeddings

RNN W2L03 Properties of Word Embeddings

RNN W2L04 Embedding matrix

One hot representation in Keras Python | One hot encoding

RNN W2L05 Learning word embeddings

Embedding Layer Keras | Embedding as a Layer | Word Embedding

RNN W2L06 Word2Vec

RNN W2L07 Negative Sampling

RNN W2L08 GloVe word vectors

RNN W2L09 Sentiment Classification

RNN W2L10 Debiasing word embeddings

RNN W3L01 Basic Models

RNN W3L02 Picking the most likely sentence

RNN W3L03 Basic Search

RNN W3L04 Refinements to Beam

RNN W3L05 Error Analysis in Beam

RNN W3L06 Bleu Score Optional

RNN W3L07 Attention Model Intuition

RNN W3L08 Attention Model

RNN W3L09 Speech Recognition

RNN W3L10 Trigger Word Detection

RNN W3L11 Conclusion and Thank you

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