MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.
Compare vanishing gradient problem case by case.
Fully connected neural network for digit classification using MNIST data
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
This project predicts used car prices using a feedforward neural network regression model implemented in PyTorch. Features include car age, mileage, and other attributes. The pipeline supports feature normalization, train/validation/test splitting, and visualization of training and validation loss curves.
Using advanced deep learning techniques on the MNIST dataset. Over 98% validation set accuracy.
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