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Anomaly Detection Using Deep Learning and Autoencoders

In this project, we will learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.

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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.


  • 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 hackerday is about Role of Autoencoders in performing classification and optimizing the hyperparameters.