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A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.
I executed this assignment for a US-based housing company named Surprise Housing, wherein a regression model with regularisation was used to predict the actual value of the prospective properties and decide whether to invest in them or not
The dataset that I am performing this regression analysis on, comes from Kaggle, titled crimes In India. This dataset holds complete information about various aspects of crimes that have taken place in India in a 17 year span, from 2001 to 2018.
The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.