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Understanding the problem statement

Importing the dataset and importing libraries

Performing basic EDA and checking for null values

Imputing the null values filling them using appropriate method

Using ggplot to visualize the Dataset

Importing h2o library and initializing an h2o cluster

Splitting Dataset into Train and Test

Defining parameters for training a Neural Network

Training the neural network for making predictions

Understanding what is difference between Artificial Neural Networks and Autoencoders

How does an Autoencoder work

Loading the pre-trained Neural Network

How to Autoencode a pre-trained Neural Networks

Visualizing the effectiveness of an Autoencoded model and a Neural Networks using ggplot

Making predictions using the trained model

In this project, we are going to work on Deep Learning using H2O to predict Census income.

In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

Problem Statement

03m

Read CSV File

01m

What is Deep Learning

05m

First Steps

01m

Univariate & Bivariate examples

04m

Correlation between variables

07m

Logistic Regression to predict Anomaly

05m

Compute ROC curve from Logistic Regression Model

05m

Train, test and validation of the model

03m

Deep Learning Models to predict Anomaly

02m

Rattle

10m

Problems with Traditional Neural Networks

04m

Deep Learning Libraries in R

06m

MXNet Library

16m

H2O Library

14m

Data Exploratory Stage

10m

Initialize the Model

03m

Create Data Partition

04m

Apply AutoEncoders

07m

Analyse Model Result

08m

Saving and Loading Model

03m

Generate Predictions

10m

Generate Model with 3 layers

16m

Features Dimension

20m

Cross Train the model

09m

Anomaly Detection

23m