Anomaly Detection Using Deep Learning and Autoencoders

Anomaly Detection Using Deep Learning and Autoencoders

Deep Learning Project- Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.

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Mohamed Yusef Ahmed

Software Developer at Taske

Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More

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VP - Data Architect, CDO at Deutsche Bank

I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More

What will you learn

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

Project Description

Deep learning is an upcoming field, where we are seeing a lot of implementations in day-to-day business operations, including segmentation, clustering, forecasting, prediction or recommendation etc. Deep learning architecture has many branches and one of them is the deep neural network (DNN), the method that we are going to analyze in this deep learning project is about the role of Autoencoders in performing classification and optimizing the hyperparameters.

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Curriculum For This Mini Project

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