- Developed NN for fve way classifcation of 10k,16×16 RGBA images using softmax activation and cross entropy loss
- Improved F1-score by varying hidden layer size and depth, and employing mini-batch SGD with adaptive learning rates
- Achieved a maximum accuracy of 76% across fve diferently shaped objects using sigmoid and ReLU activation