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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

The goal of this tensorflow project is to identify hand-written digits using a trained model using the MNIST dataset. The MNIST dataset contains a large number of hand written digits and corresponding label (correct digit)

In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.

In this deep learning project, we are going to predict which team will win the NCAA basketball tournament of coming 2017 based on past historical data.

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