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Why Did We Choose Recurrent Neural Networks

5:33

Introduction

6:42

Sentiment Classification

7:23

Tokenization and Vectorization

22:02

Tokenization

22:55

Will the Session Be Recorded

24:35

Vocabulary

26:16

Vectorization

26:54

Embedding Vectors

28:21

Masking and Padding

30:24

Why Can We Not Model Sequential Data with a Multi-Layer Perceptron

35:28

Visualize the Problem Statement

36:58

Architecture

40:42

Non-Linearity

45:13

Code Walkthrough

54:26

Introduction to Recurrent Neural Network

56:28

Download the Code Zip

57:13

Configuration

59:25

Maximum Sequence Length

1:01:52

Get imdb Data Set

1:02:34

Stop Words

1:07:17

Text Vectorization Layer

1:11:16

Output Sequence Length Config

1:12:43

Binary Cross Entropy

1:23:11

Text Vectorizer

1:26:50

What Does the Embedding Layer Do

1:28:59

Decide on the Number of Optimal Layers

1:34:08
Introduction to RNNs | TensorFlow and Keras | PyImageSearch #tensorflow #keras #rnn
An Introduction to Recurrent Neural Networks with TensorFlow and Keras. Join this live stream to get an exclusive sneak peek at an upcoming blog post covering RNNs with TensorFlow and Keras. Pssst. With this tutorial PyImageSearch officially ventures into Natural Language Processing, don't tell the image models. 🤫 Blog Post: https://pyimg.co/a3dwm

Follow along using the transcript.

PyImageSearch

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