Deep Learning Projects - Anomaly Detection Using Deep Learning

Category: Education

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Deep Learning Projects- Learn how to use state-of-the-art deep learning methods and autoencoders for anomaly detection. Check out other interesting deep learning project ideas here -


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DEEP LEARNING PROJECT Anomaly Detection Using Deep Learning and Autoencoders


Deep Learning Project - Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection. Access the solution to this deep learning project by clicking here Objective of the Project


Credit Card fraud dataset from Kaggle is used in this Data Science project. Data Set details


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. Project Description


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 learn?


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 Prerequisites


Check out other interesting deep learning project ideas by clicking here

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