Deep Learning Projects

This repository contains two deep learning projects that explore the applications of neural networks in different domains:

1. Pneumonia Detection: Classification of pneumonia using Convolutional Neural Networks (CNNs).
2. Flower Classification: Identifying flower species using Multilayer Perceptrons (MLPs).

Table of Contents

• Overview
• Project 1: Pneumonia Detection
• Project 2: Flower Classification
• Technologies Used
• Setup and Usage

Overview

Deep learning has revolutionized various fields, from healthcare to image recognition. This repository showcases two implementations of deep learning models:

• A CNN-based model for detecting pneumonia from medical images.
• An MLP-based model for classifying different species of flowers.

Both projects aim to provide insights into the practical application of deep learning techniques.

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Project 1: Pneumonia Detection

This project focuses on identifying pneumonia from X-ray images using a CNN. Key features include:

• Data preprocessing and augmentation techniques to handle medical images.
• A custom CNN architecture designed for image classification tasks.
• Performance evaluation using metrics such as accuracy, precision, recall, and F1-score.

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Project 2: Flower Classification

This project demonstrates the use of an MLP to classify flowers into different categories. Highlights include:

• Feature extraction and preprocessing for structured data.
• Implementation of a fully connected neural network using an MLP.
• Training and validation with performance metrics.

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Technologies Used

• Jupyter Notebook: All implementation and analysis are documented in Jupyter Notebook files.
• Deep Learning Frameworks: Models are implemented using popular deep learning libraries (e.g., TensorFlow or PyTorch).
• Data Visualization: Tools like Matplotlib and Seaborn are used for visualizing results.

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Setup and Usage

1. Clone the repository:

   git clone https://github.com/eyabesbes/Deep-Learning.git
   cd Deep-Learning

2. Install dependencies:

   Ensure you have Python installed along with the required libraries. You can install them using:

   pip install -r requirements.txt

   (Note: Add a requirements.txt file listing all dependencies if not already present.)

3. Run the Jupyter Notebooks:

   Start Jupyter Notebook and navigate to the project directories to execute the code:

   jupyter notebook

4. Follow the instructions in each notebook to preprocess data, train models, and evaluate performance.