Sequence Classification with LSTM RNN in Python with Keras

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

Understanding the problem statement
Importing the problem statement
Installing Keras and LSTM
Installing Tensorflow
Importing the necessary libraries for applying Neural Networks
What are Recurrent Neural Networks and how do they work
Understanding basics of NLP
Performing basic EDA and checking for the null values
Making your own Neural Network from scratch
Applying LSTM without dropout and evaluating the result
Applying LSTM with dropout and evaluating the result
Creating the model with double drop out, drop out between layers and drop out within layers of LSTM
Introducing the concept of the Fully connected network to optimize the model further
Finally evaluating the model
Making predictions for the test Dataset

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.

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Curriculum For This Mini Project

Import Libraries
Sequential Model in Keras
Load Data Set - Top words
Truncate and Pad input sequences
Create a Model
Evaluate the Model
LSTM with Dropout
LSTM and Convolutional Neural Network
LSTM and Flatten
Testing Predictions