Key moments
Why Did We Choose Recurrent Neural Networks
Why Did We Choose Recurrent Neural Networks
5:33
Introduction
Introduction
6:42
Introduction
6:42
Sentiment Classification
Sentiment Classification
7:23
Tokenization and Vectorization
Tokenization and Vectorization
22:02
Tokenization
Tokenization
22:55
Tokenization
22:55
Will the Session Be Recorded
Will the Session Be Recorded
24:35
Vocabulary
Vocabulary
26:16
Vocabulary
26:16
Vectorization
Vectorization
26:54
Vectorization
26:54
Embedding Vectors
Embedding Vectors
28:21
Embedding Vectors
28:21
Masking and Padding
Masking and Padding
30:24
Masking and Padding
30:24
Why Can We Not Model Sequential Data with a Multi-Layer Perceptron
Why Can We Not Model Sequential Data with a Multi-Layer Perceptron
35:28
Visualize the Problem Statement
Visualize the Problem Statement
36:58
Architecture
Architecture
40:42
Architecture
40:42
Non-Linearity
Non-Linearity
45:13
Non-Linearity
45:13
Code Walkthrough
Code Walkthrough
54:26
Code Walkthrough
54:26
Introduction to Recurrent Neural Network
Introduction to Recurrent Neural Network
56:28
Download the Code Zip
Download the Code Zip
57:13
Download the Code Zip
57:13
Configuration
Configuration
59:25
Configuration
59:25
Maximum Sequence Length
Maximum Sequence Length
1:01:52
Maximum Sequence Length
1:01:52
Get imdb Data Set
Get imdb Data Set
1:02:34
Get imdb Data Set
1:02:34
Stop Words
Stop Words
1:07:17
Stop Words
1:07:17
Text Vectorization Layer
Text Vectorization Layer
1:11:16
Text Vectorization Layer
1:11:16
Output Sequence Length Config
Output Sequence Length Config
1:12:43
Output Sequence Length Config
1:12:43
Binary Cross Entropy
Binary Cross Entropy
1:23:11
Binary Cross Entropy
1:23:11
Text Vectorizer
Text Vectorizer
1:26:50
Text Vectorizer
1:26:50
What Does the Embedding Layer Do
What Does the Embedding Layer Do
1:28:59
What Does the Embedding Layer Do
1:28:59
Decide on the Number of Optimal Layers
Decide on the Number of Optimal Layers
1:34:08
Sync to video time
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