Deep_Neurals

This contains deep learning and neural network projects

# 🧠 Deep_Neurals

This repository is a comprehensive collection of diverse deep learning and neural network projects, covering various domains including computer vision, natural language processing (NLP), and healthcare modeling. It serves as a practical codebase for implementing and experimenting with modern neural network architectures.

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## ✨ Key Features & Projects

This repository is organized into modules, each focusing on a specific deep learning task or model. Key projects included are:

### Computer Vision
* **Image Classification:** Implementations for classifying images using various models and techniques.
* **CNN with PyTorch (`CNN_torchvision`):** Convolutional Neural Network (CNN) projects leveraging the power of the PyTorch framework and its `torchvision` library for tasks like image recognition.
* **Object Detection (`Object_detection_torchvision`):** Practical examples and code for locating and classifying objects within images using `torchvision` models.
* **Ultralytics (YOLO):** Integration and usage of **Ultralytics** models, likely focusing on state-of-the-art YOLO (You Only Look Once) architectures for high-performance object detection.
* **Handwriting OCR (`Handwriting_OCR`):** A project dedicated to Optical Character Recognition (OCR) for recognizing handwritten text.
* **Image Colorization (Keras):** Implementation of deep learning models in Keras for automatically adding color to black-and-white images.

### Natural Language Processing (NLP) & Core Models
* **Llama LLM (`Llama_LLM`):** Projects involving the fine-tuning or utilization of the Llama Large Language Model for generative and comprehension tasks.
* **Transformers:** Implementations and usage of the popular **Transformer** architecture, which is foundational to modern NLP.
* **Core Neural Networks (`Neural_Network`):** Fundamental implementations of classic neural network layers and architectures for educational and foundational purposes.

### Specialized Applications
* **PyHealth Models (`Pyhealth_models`):** Deep learning models specifically tailored for healthcare applications, likely using the PyHealth library for clinical data analysis.
* **Identification Models (`Identification_models`):** Projects focused on using neural networks for various identification tasks (e.g., face, biometric, or pattern recognition).

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## 🚀 Getting Started

To get a local copy up and running, follow these simple steps.

### Prerequisites

You will need Python 3.7+ installed. We recommend using a virtual environment.

```bash
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Linux/macOS
# .\venv\Scripts\activate # On Windows (PowerShell)

Installation

Clone the repository and install the necessary dependencies. Since this repo covers multiple projects, dependencies may vary, but the general requirement is PyTorch, Keras, TensorFlow, and common ML libraries.

# 1. Clone the repo
git clone [https://github.com/INEcodes/Deep_Neurals.git](https://github.com/INEcodes/Deep_Neurals.git)
cd Deep_Neurals

# 2. Install common requirements (adjust based on specific project needs)
pip install torch torchvision torchaudio
pip install tensorflow keras
pip install numpy pandas matplotlib jupyter
# Additional installs for specialized projects (e.g., Pyhealth, Ultralytics) may be required.

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💻 Usage

Navigate into the specific project directory you wish to explore. Most projects are implemented as Jupyter Notebooks or Python scripts.

Example: Running the Object Detection Project

1. Navigate to the object detection directory:

   cd Object_detection_torchvision

2. Run the main script or open the notebook:

   jupyter notebook object_detection_analysis.ipynb
   # OR
   python run_detection.py 

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📂 Project Structure

The repository is structured by project type, making it easy to find relevant code:

Deep_Neurals/
├── CNN_torchvision/            # PyTorch-based CNN examples
├── Handwriting_OCR/            # OCR implementation
├── Identification_models/      # Various identification tasks
├── Image_classification/       # General image classification models
├── Image_colorization/         # Keras implementation of image colorization
│   └── Keras_implementation/
├── Llama_LLM/                  # Large Language Model experiments
├── Neural_Network/             # Foundational NN concepts and implementations
├── Object_detection_torchvision/ # PyTorch object detection
├── Pyhealth_models/            # Health/Medical AI projects
├── Transformers/               # Transformer architecture implementations
├── Ultralytics/                # YOLO/Object Detection projects (e.g., YOLOv8)
├── data/                       # Datasets or data loaders
├── cnn_architecture.png        # Visualization of a CNN model architecture
└── yolov8n.pt                  # Pre-trained YOLOv8 weights (example file)

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'feat: Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request