Convolutional Neural Networks (CNNs) from Scratch

Welcome to the Convolutional Neural Networks (CNNs) from Scratch repository! In this project, we explore the inner workings of CNNs by building them from the ground up. Whether you're a beginner eager to learn or an experienced developer seeking to customize neural networks, this repository has you covered.

Table of Contents

• Introduction
• Features
• Contents
• Prerequisites
• Getting Started
• Implementation Details
• Contributing

Introduction

Convolutional Neural Networks (CNNs) have revolutionized image processing and recognition tasks. However, understanding how they work internally can be just as crucial as using pre-built libraries. This repository provides detailed code for creating CNNs from scratch, covering essential components like convolution layers, pooling layers, forward passes, and backward passes.

Features

• Fundamental Learning: Dive deep into the basics of CNNs and develop a solid understanding of the architecture.
• Step-by-Step Explanations: Every code block is explained to help learners grasp the underlying concepts.
• Convolution Layers: Create convolution layers that extract features from input images.
• Pooling Layers: Implement max-pooling and average-pooling layers to downsize feature maps.
• Forward and Backward Passes: Build both forward and backward passes for training your CNN.
• Great for Beginners and Experts: Whether you're new to CNNs or want to customize your networks, this project caters to all levels.

Contents

1. Convolution Layer: We'll explore the essentials of convolution layers and create them from scratch.
2. Pooling Layer: Dive into max-pooling and average-pooling layers and learn how they reduce input size.
3. Forward Pass: Implement the forward pass to compute CNN output.
4. Backward Pass: Develop the backward pass to calculate gradients and fine-tune your network.

Prerequisites

Before you get started, ensure you have the following installed:

• Python (3.5+)
• NumPy
• Jupyter Notebook (for running the notebook)

Getting Started

1. Clone this repository to your local machine.

   git clone https://github.com/yourusername/ConvNetBuilder.git

2. Open the Jupyter Notebook CNN_from_Scratch.ipynb to start exploring and running the code.

3. Follow along with the step-by-step instructions to build your CNN from scratch and understand how it works.

Implementation Details

You can find detailed implementation notes in the Jupyter Notebook. Each section contains explanations, code, and examples to aid your learning.

Contributing

Contributions to enhance this project are highly welcome! If you have improvements, suggestions, or want to fix issues, please submit a pull request. For major changes, please open an issue first to discuss the proposed changes.