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https://github.com/elprofesoriqo/ml-optimizer
Python library designed to revolutionize machine learning workflows by automating data preprocessing, tensor optimization, and model selection.
https://github.com/elprofesoriqo/ml-optimizer
api-rest cuda imagesearch machine-learning machine-learning-algorithms numpy-arrays python pytorch tensor
Last synced: 10 days ago
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Python library designed to revolutionize machine learning workflows by automating data preprocessing, tensor optimization, and model selection.
- Host: GitHub
- URL: https://github.com/elprofesoriqo/ml-optimizer
- Owner: elprofesoriqo
- Created: 2024-12-11T14:21:15.000Z (30 days ago)
- Default Branch: main
- Last Pushed: 2024-12-30T14:08:09.000Z (11 days ago)
- Last Synced: 2024-12-30T15:20:28.334Z (11 days ago)
- Topics: api-rest, cuda, imagesearch, machine-learning, machine-learning-algorithms, numpy-arrays, python, pytorch, tensor
- Language: Python
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ML Optimizer: Intelligent Data Processing and Machine Learning Model Optimizer
## 🚀 Project Overview
ML Optimizer is an innovative Python library designed to revolutionize machine learning workflows by automating data preprocessing, tensor optimization, and model selection.
## 🎯 Key Features
- **Universal Data Processing**: Supports multiple input formats
- **Intelligent Tensor Compression**: Advanced compression techniques
- **Adaptive Model Selection**: Automatic model architecture recommendation
- **Performance Optimization**: Resource-efficient machine learning pipeline
## 📦 Installation
```bash
# Clone the repository
git clone https://github.com/your-username/ml-optimizer.git
# Install dependencies
pip install -r requirements.txt
# Install the package
pip install .
from ml_optimizer import MLOptimizer
# Load your data from any source
data = load_your_data() # Supports various input types
# Initialize the optimizer
optimizer = MLOptimizer(verbose=True)
# Automatic data and model optimization
result = optimizer.optimize_pipeline(data)
# Analyze performance
optimizer.analyze_performance(result)
```
🛠 Quick Start
```bash
pythonCopyfrom ml_optimizer import MLOptimizer
# Load your data from any source
data = load_your_data() # Supports various input types
# Initialize the optimizer
optimizer = MLOptimizer(verbose=True)
# Automatic data and model optimization
result = optimizer.optimize_pipeline(data)
# Analyze performance
optimizer.analyze_performance(result)
```
# 🧠 Core Components
## 1. Data Processing Module
- Supports multiple input formats
- Automatic data normalization
- Multi-layer data validation
## 2. Tensor Compression Module
- Quantization techniques
- Dimensionality reduction
- Irrelevant feature elimination
## 3. Model Selection Module
- Architecture selection heuristics
- Adaptive hyperparameter tuning
- Data characteristic analysis
# 🔍 Supported Input Types
- PyTorch Tensors
- NumPy Arrays
- CSV files
- JSON
- Image files
- Audio data
- Sequential data
# 🖥 System Requirements
- **Python** 3.9+
- **PyTorch** 1.10+
- Minimum 8 GB RAM
- Optional: CUDA-enabled GPU
# 📊 Performance Metrics
The library aims to:
- Reduce data preprocessing time by up to 50%
- Minimize computational resource usage
- Improve model selection accuracy
# 💡 Future Enhancements
- Federated learning support
- Cloud ML service integration
- Automated hyperparameter optimization
- Web visualization interface
# 🌟 Support
If you find this project useful, please consider:
- ⭐ Starring the repository
- 🍴 Forking the project
- 💡 Contributing to the development