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Sequence Classification with LSTM RNN in Python with Keras

In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

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What will you learn

  • Implementation of a sequence to sequence model
  • Implementation of RNN using keras
  • Implementation of CNN using keras
  • Implementation of LSTM
  • Benchmarking of algorithms

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • ​Installation of Keras and Tensorflow
  • Language used: Python

Project Description

A sequence to sequence prediction for developing a classification system is of very much required in developing applications. Standard approaches for developing applications won't help in providing accuracy. Hence, as an example let's take an IMDB movie review dataset and create some benchmarks by using RNN, RNN with LSTM and drop out rate, RNN with CNN, and RNN with CNN plus drop out rate to make a composite sequence to sequence classification work. We can compare the model accuracy as well.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Introduction
00:01:09
  Import Libraries
00:00:59
  Sequential Model in Keras
00:02:53
  Load Data Set - Top words
00:01:45
  Truncate and Pad input sequences
00:06:45
  Create a Model
00:25:49
  Evaluate the Model
00:03:09
  LSTM with Dropout
00:10:22
  Recap
00:00:54
  LSTM and Convolutional Neural Network
00:20:20
  LSTM and Flatten
00:16:15
  Conclusion
00:02:33
  Testing Predictions
00:04:12