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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
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.