https://github.com/ornella-gigante/ia_deep_learning
Solución de problemas reales- en este clasificando enfermedades cardiovasculares e imágenes- a través de un modelo FEED -FORWARD.
https://github.com/ornella-gigante/ia_deep_learning
ai airflow bootcamp-project classify-images deep-learning deep-neural-networks feedforward-neural-network
Last synced: 2 months ago
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Solución de problemas reales- en este clasificando enfermedades cardiovasculares e imágenes- a través de un modelo FEED -FORWARD.
- Host: GitHub
- URL: https://github.com/ornella-gigante/ia_deep_learning
- Owner: Ornella-Gigante
- License: gpl-3.0
- Created: 2024-02-28T18:12:03.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T17:40:32.000Z (3 months ago)
- Last Synced: 2025-03-08T12:47:57.421Z (2 months ago)
- Topics: ai, airflow, bootcamp-project, classify-images, deep-learning, deep-neural-networks, feedforward-neural-network
- Language: Jupyter Notebook
- Homepage:
- Size: 85.9 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧠 IA_Deep_Learning: Real Problem Solving with Deep Learning
Welcome to **IA_Deep_Learning**! This repository showcases a project focused on solving real-world problems using deep learning techniques, specifically through a **FEED-FORWARD** model. Here's what you need to know:
## 🚀 Project Overview
- **Domain**: Deep Learning, Artificial Intelligence
- **Language**: Python
- **Frameworks**: TensorFlow, Keras
- **Purpose**: To classify cardiovascular diseases and images using a feed-forward neural network.
## 🌟 Key Features
- **Cardiovascular Disease Classification**: Utilizes a feed-forward model to predict the presence of cardiovascular diseases based on patient data.
- **Image Classification**: Applies the same model architecture to classify images, demonstrating the versatility of feed-forward networks.
- **Data Preprocessing**: Includes steps for data cleaning, normalization, and feature engineering to prepare datasets for model training.
- **Model Evaluation**: Provides metrics and visualizations to assess model performance, including accuracy, precision, recall, and F1-score.
## 🛠️ How to Use
1. **Clone the Repository**:
git clone https://github.com/Ornella-Gigante/IA_Deep_Learning.git
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2. **Setup Environment**:
- Ensure you have Python installed.
- Install required libraries using `pip install -r requirements.txt`.
3. **Run the Project**:
- Navigate to the project directory.
- Execute the main script to train and evaluate the model.
4. **Experiment and Modify**:
- Adjust the model architecture, hyperparameters, or dataset to explore different scenarios or improve performance.
## 📚 Learning and Contribution
This project is an excellent resource for:
- **Deep Learning**: Understand how feed-forward neural networks work and their applications in real-world problems.
- **Data Science**: Learn about data preprocessing, feature selection, and model evaluation techniques.
- **Python Programming**: Enhance your Python skills by working with deep learning libraries.
Feel free to:
- **Fork** the repository and make your own changes or improvements.
- **Contribute** by submitting pull requests with new features, bug fixes, or enhancements.
- **Report Issues** if you encounter any problems or have suggestions for the project.
## 👩💻 Author
- **Ornella Gigante** - *Creator and Maintainer*
## 📜 License
This project is open-sourced under the [MIT License](LICENSE). You are free to use, modify, and distribute the code as per the license terms.
## 🌐 Connect
- [LinkedIn](https://www.linkedin.com/in/ornella-gigante/)
Let's tackle real-world problems with the power of deep learning! 🎉