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🧠 Convolutional Neural Networks: Step by Step

Welcome to the Convolutional Neural Networks: Step by Step repository! 🚀 This project provides an in-depth implementation of foundational concepts in Convolutional Neural Networks (CNNs), including zero-padding, convolutional steps, forward propagation, and pooling layers.

This repository is an excellent resource for anyone looking to understand the mechanics of CNNs through practical coding examples. 💻✨

📖 Table of Contents

  1. Introduction
  2. Getting Started
  3. Core Concepts
    • Zero-Padding
    • Single-Step Convolution
    • Forward Propagation
    • Pooling Layers
  4. How to Use
  5. Acknowledgments

🌟 Introduction

Convolutional Neural Networks are powerful deep learning models used for image processing, computer vision, and more. This repository demonstrates:

  • Applying zero-padding to input images.
  • Performing a single convolution step.
  • Conducting forward propagation in a convolutional layer.
  • Implementing pooling operations (max and average pooling).

These examples are implemented in Python using NumPy and Matplotlib, providing a step-by-step guide to the foundational aspects of CNNs. 📊📸

⚙️ Getting Started

Prerequisites

  • Python 3.x
  • Libraries: NumPy, Matplotlib, h5py

Installation

Clone this repository and navigate to the project directory:

git clone https://github.com/MohammedSaqibMS/Convolution_model_Step_by_Step.git
cd Convolution_model_Step_by_Step

Install the required dependencies:

pip install numpy matplotlib h5py

🔍 Core Concepts

1️⃣ Zero-Padding

Zero-padding adds borders of zeros around input images to preserve spatial dimensions during convolution operations.

2️⃣ Single-Step Convolution

A single convolution step applies a filter (weight matrix) to a slice of the input data, producing an output scalar.

3️⃣ Forward Propagation

This involves applying filters and biases across the input data to produce feature maps for the next layer.

4️⃣ Pooling Layers

Pooling layers downsample feature maps, reducing their size while retaining important information. Supported modes: max pooling and average pooling.

🛠️ How to Use

Run the Python scripts in sequence to execute each step of the CNN operations:

python zero_padding.py
python single_step_convolution.py
python conv_forward.py
python pool_forward.py

Explore the visualization outputs for better understanding! 🖼️

🙌 Acknowledgments

Special thanks to the amazing team at DeepLearning.AI for their educational resources and inspiration. This repository is built upon concepts taught in their Deep Learning Specialization course. 🎓📚

📬 Contact

For any questions or suggestions, feel free to reach out via email or create an issue in the repository. Let's learn and grow together! 🌱✨