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
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Some of the topics, algorithms and projects in Machine Learning & Deep Learning that I have worked on and become familiar with.
- Host: GitHub
- URL: https://github.com/awrsha/advanced-deep-learning-ai-projects
- Owner: Awrsha
- License: mit
- Created: 2024-04-07T23:27:37.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-11-19T19:58:19.000Z (11 months ago)
- Last Synced: 2025-04-02T13:11:17.575Z (6 months ago)
- Topics: adaboost, analysis, artificial-intelligence, classification, data-engineering, decision-trees, evaluation, neural-networks, optimization, prediction, preprocessing, principal-component-analysis, random-forest, regression, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 77.1 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧠 Advanced Deep Learning & AI Projects Collection
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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 monitoringVision 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)