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Anomaly Detection Using Deep Learning and Autoencoders

Deep Learning Project- Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.
4.64.6

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What will you learn

  • How to develop a baseline of performance for a classification problem.
  • What technique/algorithms to be used in unbalanced data scenario
  • What are the accuracy measures in a deep learning framework
  • How to configure H2O in R-Studio to run deep learning
  • Implementation and hyperparameter optimization

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Jupyter Notebook from Anaconda installation
  • R (3.3.3) and R-Studio (1.4) installation
  • At least 5mbps internet speed
  • At least 4 GB RAM Machine

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.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Problem Statement
00:03:29
  Read CSV File
00:01:20
  What is Deep Learning
00:05:25
  First Steps
00:01:09
  Univariate & Bivariate examples
00:04:41
  Correlation between variables
00:07:53
  Logistic Regression to predict Anomaly
00:05:54
  Compute ROC curve from Logistic Regression Model
00:05:53
  Train, test and validation of the model
00:03:28
  Deep Learning Models to predict Anomaly
00:02:24
  Rattle
00:10:12
  Problems with Traditional Neural Networks
00:04:43
  Deep Learning Libraries in R
00:06:14
  MXNet Library
00:16:04
  H2O Library
00:14:17
  Data Exploratory Stage
00:10:36
  Initialize the Model
00:03:09
  Create Data Partition
00:04:32
  Apply AutoEncoders
00:07:51
  Analyse Model Result
00:08:03
  Saving and Loading Model
00:03:47
  Generate Predictions
00:10:32
  Generate Model with 3 layers
00:16:12
  Features Dimension
00:20:08
  Cross Train the model
00:09:55
  Anomaly Detection
00:23:02