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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SUBHABRATA BISWAS

Lead Consultant, ITC Infotech

The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More

Shailesh Kurdekar

Solutions Architect at Capital One

I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More

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

Introduction
01m
Import Libraries
00m
Sequential Model in Keras
02m
Load Data Set - Top words
01m
Truncate and Pad input sequences
06m
Create a Model
25m
Evaluate the Model
03m
LSTM with Dropout
10m
Recap
00m
LSTM and Convolutional Neural Network
20m
LSTM and Flatten
16m
Conclusion
02m
Testing Predictions
04m