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Sequence Models Natural Language

Track :

Programming

Lessons no : 36

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Lessons | 36
RNN 1 Recurrent Neural Networks — Introduction | Motivation | Why Sequence Models
00:06:24

RNN 1 Recurrent Neural Networks — Introduction | Motivation | Why Sequence Models
RNN 2 Notation | Sentence and Word Representation | Sequence Models
00:07:45

RNN 2 Notation | Sentence and Word Representation | Sequence Models
RNN 3 Recurrent Neural Network Model | RNN Model
00:14:26

RNN 3 Recurrent Neural Network Model | RNN Model
RNN 4 Backpropagation through time | BPTT | RNN
00:08:20

RNN 4 Backpropagation through time | BPTT | RNN
RNN W1L05 Different types of RNNs
00:09:34

RNN W1L05 Different types of RNNs
RNN W1L06 Language Model and sequence generation
00:12:02

RNN W1L06 Language Model and sequence generation
RNN W1L07 Sampling novel Sequences
00:08:39

RNN W1L07 Sampling novel Sequences
RNN W1L08 Vanishing gradients with RNNs
00:06:29

RNN W1L08 Vanishing gradients with RNNs
RNN W1L09 Gated Recurrent Unit GRU
00:17:07

RNN W1L09 Gated Recurrent Unit GRU
RNN W1L10 Long Short Term Memory LSTM
00:09:54

RNN W1L10 Long Short Term Memory LSTM
LSTM in Keras | Understanding LSTM input and output shapes
00:11:20

LSTM in Keras | Understanding LSTM input and output shapes
RNN W1L11 Bidirectional RNN
00:08:20

RNN W1L11 Bidirectional RNN
RNN W1L12 Deep RNNs
00:05:17

RNN W1L12 Deep RNNs
RNN W2L01 Word Representation
00:10:08

RNN W2L01 Word Representation
RNN W2L02 Using Word Embeddings
00:09:23

RNN W2L02 Using Word Embeddings
RNN W2L03 Properties of Word Embeddings
00:11:55

RNN W2L03 Properties of Word Embeddings
RNN W2L04 Embedding matrix
00:05:58

RNN W2L04 Embedding matrix
One hot representation in Keras Python | One hot encoding
00:10:01

One hot representation in Keras Python | One hot encoding
RNN W2L05 Learning word embeddings
00:10:09

RNN W2L05 Learning word embeddings
Embedding Layer Keras | Embedding as a Layer | Word Embedding
00:15:18

Embedding Layer Keras | Embedding as a Layer | Word Embedding
RNN W2L06 Word2Vec
00:12:48

RNN W2L06 Word2Vec
RNN W2L07 Negative Sampling
00:11:54

RNN W2L07 Negative Sampling
RNN W2L08 GloVe word vectors
00:11:09

RNN W2L08 GloVe word vectors
RNN W2L09 Sentiment Classification
00:07:38

RNN W2L09 Sentiment Classification
RNN W2L10 Debiasing word embeddings
00:11:09

RNN W2L10 Debiasing word embeddings
RNN W3L01 Basic Models
00:06:19

RNN W3L01 Basic Models
RNN W3L02 Picking the most likely sentence
00:08:57

RNN W3L02 Picking the most likely sentence
RNN W3L03 Basic Search
00:11:55

RNN W3L03 Basic Search
RNN W3L04 Refinements to Beam
00:11:01

RNN W3L04 Refinements to Beam
RNN W3L05 Error Analysis in Beam
00:09:44

RNN W3L05 Error Analysis in Beam
RNN W3L06 Bleu Score Optional
00:16:27

RNN W3L06 Bleu Score Optional
RNN W3L07 Attention Model Intuition
00:09:42

RNN W3L07 Attention Model Intuition
RNN W3L08 Attention Model
00:12:23

RNN W3L08 Attention Model
RNN W3L09 Speech Recognition
00:08:54

RNN W3L09 Speech Recognition
RNN W3L10 Trigger Word Detection
00:05:03

RNN W3L10 Trigger Word Detection
RNN W3L11 Conclusion and Thank you
00:02:45

RNN W3L11 Conclusion and Thank you


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Sequence models are the machine learning models that input or output sequences of data. Sequential data includes text streams, audio clips, video clips, time-series data and etc. Recurrent Neural Networks (RNNs) is a popular algorithm used in sequence models .