Image generation using Super Resolution GAN architecture

Understanding the Generative Adversarial Network

Generative Adversarial Network which is popularly known as GANs is a deep learning, unsupervised machine learning technique which is proposed in year 2014 through this research paper. The main blocks of this architecture are ;

  1. Generator : This block tries to generates the images which are very similar to that of original dataset by taking noise as input. It tries to learn the join probability of the input data (X) and output data(Y); P(X|Y).

Overview of Problem :

Short Message Service or SMS considered to be the text messaging service component of Telephone or Internet. In our day-to-day life we do receive considerable amount of SMS either from Friends, Telecom or Bank companies regarding our daily transactions or from tons of other sources. Some of these SMS texts are genuine whereas some can lead to fraudulent incidents.

Main task of this case study is making a Machine Learning model which can predict the SMS as HAM or SPAM with the help of text body of SMS. Dataset for this case study can be found…

Introduction and Overview of the Problem

Web Traffic Forecasting is a problem of forecasting the future page views that we can receive for given Wikipedia articles. The prediction to be carried out based on the available time series data. The data for this problem can be downloaded from here.


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