Deep-Learning-for-simple-classification-model
1. Load the attached csv file in python. Each row consists of feature 1, feature 2, class label. 2. Train two single/double hidden layer deep networks by varying the number of hidden nodes (4, 8, 12, 16) in each layer with 70% training and 30% validation data. Use appropriate learning rate, activation, and loss functions and also mention the reason for choosing the same. Report, compare, and explain the observed accuracy and minimum loss achieved. [0.5+1 mark] 3. Visually observe the dataset and design an appropriate feature transformation (derived feature) such that after feature transformation, the dataset can be classified using a minimal network architecture (minimum number of parameters). Design, train this minimal network, and report training and validation errors, and trained parameters of the network. Use 70% training and 30% validation data, appropriate learning rate, activation and loss functions. Explain the final results.
