An open API service indexing awesome lists of open source software.

https://github.com/awrsha/advanced-deep-learning-ai-projects

Some of the topics, algorithms and projects in Machine Learning & Deep Learning that I have worked on and become familiar with.
https://github.com/awrsha/advanced-deep-learning-ai-projects

adaboost analysis artificial-intelligence classification data-engineering decision-trees evaluation neural-networks optimization prediction preprocessing principal-component-analysis random-forest regression xgboost

Last synced: 6 months ago
JSON representation

Some of the topics, algorithms and projects in Machine Learning & Deep Learning that I have worked on and become familiar with.

Awesome Lists containing this project

README

          

# 🧠 Advanced Deep Learning & AI Projects Collection








A comprehensive collection of cutting-edge AI & Deep Learning implementations

## 📚 Table of Contents

- [Overview](#-overview)
- [Project Categories](#-project-categories)
- [Key Features](#-key-features)
- [Projects in Detail](#-projects-in-detail)
- [Installation](#-installation)
- [Usage](#-usage)
- [Contributing](#-contributing)
- [License](#-license)

## 🌟 Overview

A state-of-the-art collection of AI and Deep Learning projects, showcasing various architectures and applications in computer vision, natural language processing, and more.

## 📊 Project Categories

### 🖼️ Computer Vision
- [Object Detection & Counting (Faster R-CNN)](#object-detection)
- [Vision Transformer Classification](#vision-transformer)
- [Pneumonia Detection (EfficientNet)](#medical-imaging)
- [Violence Detection in Videos](#video-analysis)
- [Image Captioning](#image-captioning)
- [Question-Answering System](#qa-system)
- [Intent Classification](#intent-classification)

### 🧮 Neural Networks & Deep Learning
- [AdaLine & MadaLine](#adaptive-learning)
- [Multi-Layer Perceptron](#mlp)
- [Auto-Encoders (MNIST)](#auto-encoders)
- [Conditional DC-GAN](#generative-models)

### 📈 Applied AI
- [Cryptocurrency Price Prediction (RNN)](#crypto-prediction)
- [Dimensionality Reduction Study](#dimension-reduction)

## 💫 Key Features




🔥 State-of-the-Art Models
📊 Performance Metrics
📝 Detailed Documentation


🔄 Easy-to-use APIs
⚡ Optimized Performance
🎯 Practical Applications


## 📂 Projects in Detail

### 🔍 Computer Vision Projects

Object Detection & Counting

- Framework: Faster R-CNN
- Features:
- Real-time object detection
- Multiple object tracking
- Counting mechanism
- Applications:
- Surveillance
- Retail analytics
- Traffic monitoring

Vision Transformer

- Architecture: ViT
- Features:
- Attention mechanism
- Patch-based processing
- State-of-the-art accuracy

- Architecture: CNN + LSTM
- Features:
- Automatic caption generation
- Attention mechanism
- Beam search decoding

### 🧮 Deep Learning Projects

Conditional DC-GAN

- Features:
- Conditional generation
- High-quality synthetic data
- Controlled attributes

## 🛠️ Installation

```bash
# Clone the repository
git clone https://github.com/Awrsha/deep-learning-projects.git

# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows

# Install dependencies
pip install -r requirements.txt
```

## 📖 Usage

Each project includes detailed documentation and example notebooks:

1. Navigate to project directory
2. Review README.md for specific requirements
3. Run Jupyter notebooks for demonstrations
4. Check documentation for API usage

## 🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

### 🌐 Connect & Follow

[![GitHub][]](https://github.com/Awrsha)[![LinkedIn][]](https://linkedin.com/in/awrsha)[![Website][]](https://awrsha.github.io)