Implementation-of-MLP-with-Numpy

Implementation of Multi-Layer Perceptron with Numpy and CNN with PyTorch in Image Classification

Image classification

Implementation From Scratch (Numpy)

Multi-Layer Perceptron (MLP/Neuron Networks) with backpropagation and mini-batch SGD

Activations:
- Relu
- Sigmoid
- Softmax
- Leaky_relu
Layers:
- Forward
- Backward
- Number of layers determined by user

AND

Pytorch CNN Convoluntional Neuron Network

Layers:
- Convoluntional layer (Extract different features from various feature maps)
- Pooling layer (Max pooling - average pooling: extract dominant features, speed up training speed)
- Batch Normalization
- Dropout

Data used:

CIFAR-10 with 10 classes