Convolutional Neural Network from Scratch

This repository contains a project aimed at building a Convolutional Neural Network (CNN) from scratch using NumPy. The project focuses on implementing key components of a neural network, such as the ReLU activation function, fully connected layers, and the convolutional and pooling layers. The goal is to develop an end-to-end pipeline for image classification tasks, demonstrating a deep understanding of CNNs by implementing the architecture from first principles.

This project involves building the following components:

1. ReLU Activation: A simple activation function to introduce non-linearity.
2. Fully Connected Layer: Standard dense layers with backpropagation.
3. Convolutional Layer: Implements convolutions for extracting spatial features.
4. Pooling Layer: Includes max pooling and average pooling to downsample feature maps.
5. Cross-Entropy Loss: Loss function for classification tasks.
6. Softmax Activation: Converts output scores into probabilities for multi-class classification.

The project aims to provide an understanding of how CNNs work from the ground up by avoiding deep learning libraries such as TensorFlow or PyTorch in core implementations.