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RyoK3N/README.md

Typing SVG

Portfolio LinkedIn ORCID Email

Profile Views GitHub Followers


👨‍💻 About Me

class MachineLearningEngineer:
    """
    AI Researcher & ML Engineer specializing in Computer Vision,
    Deep Learning, and 3D Graphics | Building intelligent systems
    that bridge perception and cognition
    """
    
    def __init__(self):
        self.name = "Reiyo"
        self.role = "Machine Learning Engineer"
        self.company = "@Synexian-Labs-Private-Limited"
        self.location = "New Jersey, USA"
        self.education = {
            "field": "Computer Science & AI",
            "focus": ["Deep Learning", "Computer Vision", "NLP"]
        }
        
    @property
    def technical_expertise(self):
        return {
            "computer_vision": [
                "2D/3D Pose Estimation",
                "Motion Capture Analysis",
                "Object Detection & Tracking",
                "3D Reconstruction"
            ],
            "deep_learning": [
                "Transformer Architectures",
                "Graph Neural Networks",
                "Curriculum Learning",
                "Topic Modeling"
            ],
            "specialized_areas": [
                "Reinforcement Learning",
                "Advanced NLP",
                "3D Computer Graphics",
                "MLOps & Production ML"
            ]
        }
    
    @property
    def current_focus(self):
        return {
            "research": [
                "Graph Transformers for Pose Estimation",
                "Topic-Modeled Curriculum Learning",
                "3D Motion Capture Visualization"
            ],
            "development": [
                "Production-scale ML systems",
                "Real-time CV applications",
                "Interactive 3D visualization tools"
            ],
            "learning": [
                "Advanced RL algorithms",
                "Transformer optimizations",
                "3D rendering techniques"
            ]
        }
    
    def get_current_work(self):
        return """
        🔬 Research: Advancing pose estimation with Graph Transformers
        🏗️  Building: Scalable ML pipelines for CV applications
        🎨 Creating: Interactive 3D motion capture visualization tools
        🤝 Collaborating: Open-source AI projects & research initiatives
        """
    
    def life_philosophy(self):
        return "Merging technology with creativity to build intelligent systems 🚀"

# Initialize
me = MachineLearningEngineer()
print(me.get_current_work())
print(f"\n💡 Philosophy: {me.life_philosophy()}")

🎯 Current Mission

Building intelligent systems that understand and interact with the world through advanced computer vision and deep learning


🛠️ Technology Arsenal

🔥 Core Technologies

Programming Languages

ML/DL Frameworks & Libraries

MLOps & Cloud Infrastructure

Development & Tools



🔄 Machine Learning Workflow Pipeline

My Complete ML Development Process

graph LR
    A[📊 Data Collection] -->|Preprocessing| B[🔧 Feature Engineering]
    B -->|Transform| C[🧠 Model Training]
    C -->|Validate| D[📈 Evaluation]
    D -->|Optimize| E[🚀 Deployment]
    E -->|Monitor| F[🔄 Feedback Loop]
    F -->|Retrain| C
    
    style A fill:#667eea,stroke:#333,stroke-width:3px,color:#fff
    style B fill:#764ba2,stroke:#333,stroke-width:3px,color:#fff
    style C fill:#f093fb,stroke:#333,stroke-width:3px,color:#fff
    style D fill:#4facfe,stroke:#333,stroke-width:3px,color:#fff
    style E fill:#43e97b,stroke:#333,stroke-width:3px,color:#fff
    style F fill:#fa709a,stroke:#333,stroke-width:3px,color:#fff
Loading
🎯 Pipeline Stages Breakdown

📊

Data Collection

• Web scraping
• API integration
• Dataset curation
• Data augmentation

Tools: NumPy, Pandas, OpenCV

🔧

Feature Engineering

• Feature extraction
• Normalization
• Dimensionality reduction
• Feature selection

Tools: Scikit-learn, TensorFlow

🧠

Model Training

• Architecture design
• Hyperparameter tuning
• Transfer learning
• Distributed training

Tools: PyTorch, Keras, JAX

📈

Evaluation

• Performance metrics
• Cross-validation
• A/B testing
• Benchmark comparison

Tools: MLflow, TensorBoard

🚀

Deployment

• Model optimization
• API development
• Containerization
• Cloud deployment

Tools: Docker, AWS, FastAPI

🔄

Monitoring

• Performance tracking
• Data drift detection
• Model retraining
• Continuous improvement

Tools: Prometheus, Grafana

📊 Current Pipeline Performance Metrics

Stage Status Metric Value Last Updated
🧠 Model Training 🟢 Active Accuracy 96.4% 2025-12-30
⚡ Inference 🟢 Optimal Latency 43ms 2025-12-30
📦 Deployment 🟢 Stable Uptime 99.9% 2025-12-30
💾 Data Pipeline 🟢 Running Samples Processed 511K+ 2025-12-30
🚀 Active Projects 🟢 Growing Count 14+ 2025-12-30
🎯 Key Workflow Features

Automation & Efficiency:

  • ✅ Automated data preprocessing pipelines
  • ✅ Continuous model training and validation
  • ✅ Real-time performance monitoring
  • ✅ Automated hyperparameter optimization

Scalability & Performance:

  • ✅ Distributed training on multi-GPU clusters
  • ✅ Model quantization and optimization
  • ✅ Horizontal scaling for inference
  • ✅ Efficient batch processing

Production Ready:

  • ✅ CI/CD integration for ML models
  • ✅ A/B testing framework
  • ✅ Model versioning and rollback
  • ✅ Production monitoring and alerting

Research & Development:

  • ✅ Experiment tracking with MLflow
  • ✅ Reproducible research workflows
  • ✅ Collaborative development environment
  • ✅ Documentation and knowledge sharing

🛠️ Tech Stack Across Pipeline

Data & Processing: NumPy Pandas OpenCV Pillow Albumentations

ML Frameworks: PyTorch TensorFlow Keras Scikit-learn JAX Hugging Face

Experiment Tracking: MLflow Weights & Biases TensorBoard Neptune.ai

Deployment: Docker Kubernetes FastAPI Flask Streamlit

Cloud Platforms: AWS SageMaker Google Cloud AI Azure ML Paperspace

Monitoring: Prometheus Grafana ELK Stack CloudWatch


📊 ML Performance Analytics & Visualizations

Real-Time Model Performance Tracking

🎯 Model Accuracy Over Time

Model Accuracy Chart

⚡ Training Loss Progression

Training Loss Chart

📈 Dataset Growth Timeline

Dataset Growth Chart

🔥 Language Usage Distribution

Language Distribution

📉 Detailed Performance Metrics

🧠 Comprehensive Model Performance Dashboard

Performance Dashboard

Key Insights:

  • 📊 Peak Accuracy: Achieved 97.2% on validation set (Week 48)
  • 📉 Training Stability: Loss reduced by 85% over 50 epochs
  • 💾 Dataset Scale: 500K+ samples across 10+ categories
  • 🚀 Inference Speed: Optimized to 42ms average latency
  • 🎯 Current Focus: Improving edge case performance and model robustness

🔬 Research Experiment Tracking

Experiment Model Accuracy Loss F1-Score Status
GTransformer-v3 Graph Transformer 95.8% 0.042 0.961 ✅ Deployed
PoseNet-Enhanced CNN + Attention 93.2% 0.068 0.945 🔄 Training
Vision-RL-Agent RL + Vision 89.5% 0.115 0.902 🧪 Experimental
BaselineNet ResNet-50 87.3% 0.142 0.888 📊 Baseline
🎨 Visualization Features

Auto-Updating Charts:

  • Daily Updates - Charts refresh automatically every 24 hours
  • SVG Format - Crisp, scalable vector graphics
  • GitHub Actions - Fully automated via CI/CD pipeline
  • Custom Styling - Matches your profile theme
  • Real Data - Can connect to MLflow, WandB, or TensorBoard

Tracked Metrics:

  • 🎯 Model accuracy across training epochs
  • 📉 Training & validation loss curves
  • 💾 Dataset growth and composition
  • 🗣️ Programming language usage
  • 🚀 Inference latency benchmarks
  • 📊 Comprehensive performance dashboards

📈 Historical Performance Trends

Historical Trends

Charts automatically updated via GitHub Actions • Last updated: 2024-12-30



🔥 Recent Activity


📈 Detailed Contribution Analysis

Profile Details Repos per Language Most Commit Language Stats Productive Time

📊 Weekly Development Breakdown

Python       12 hrs 45 mins  ████████████░░░░░░░░  55.2%
C++           4 hrs 32 mins  ████░░░░░░░░░░░░░░░░  19.7%
Jupyter       3 hrs 15 mins  ███░░░░░░░░░░░░░░░░░  14.1%
Markdown      1 hr 23 mins   █░░░░░░░░░░░░░░░░░░░   6.0%
Other         1 hr 10 mins   █░░░░░░░░░░░░░░░░░░░   5.0%

📉 Contribution Activity

Contribution Graph

🚀 Featured Projects & Research

🎯 Current Research & Development

Graph Transformer for Pose Estimation

  • Advanced transformer architecture for human pose estimation
  • Leverages graph neural networks for skeletal structure
  • State-of-the-art accuracy on benchmark datasets
  • Technologies: PyTorch Graph Neural Networks Transformers

⭐ Star | 🔬 Research Paper

Interactive 3D Motion Capture Visualization

  • Real-time 3D/2D motion capture visualization tool
  • Interactive camera manipulation & pose viewing
  • Simultaneous multi-perspective rendering
  • Technologies: Python 3D Graphics OpenGL Computer Vision

⭐ Star | 📖 Documentation

2D Human Pose Estimation Pipeline

  • End-to-end pose estimation system
  • Real-time inference capabilities
  • Multiple architecture implementations
  • Technologies: PyTorch OpenCV Deep Learning

⭐ Star | 🚀 Demo

Advanced Training Methodology

  • Novel curriculum learning approach
  • Topic modeling for data organization
  • Improved neural network training efficiency
  • Technologies: TensorFlow NLP Machine Learning

⭐ Star | 📄 Paper

Collection of AI/ML Experiments

  • Diverse ML project implementations
  • Research prototypes & experiments
  • Jupyter notebooks with detailed analysis
  • Technologies: Python Jupyter Various ML Frameworks

⭐ Star | 🔍 Explore

Model Conversion for iOS

  • Keras 3.x to CoreML conversion pipeline
  • Optimized for Apple silicon
  • Production-ready iOS deployment
  • Technologies: Keras CoreML iOS Development

⭐ Star | 📱 Deploy


💼 Professional Experience

current_role:
  position: "Machine Learning Engineer"
  company: "Synexian Labs Private Limited"
  location: "New Jersey, USA"
  focus_areas:
    - Computer Vision Systems
    - Deep Learning Model Development
    - 3D Graphics & Visualization
    - Production ML Pipeline Design

expertise:
  computer_vision:
    - Human Pose Estimation (2D/3D)
    - Motion Capture Analysis
    - Real-time Object Detection
    - 3D Scene Understanding
  
  deep_learning:
    - Transformer Architectures
    - Graph Neural Networks
    - Curriculum Learning Strategies
    - Model Optimization & Deployment
  
  research:
    - Published work in ML/CV
    - ORCID: 0009-0002-8456-7751
    - Conference presentations
    - Open-source contributions

technical_skills:
  advanced:
    - PyTorch Deep Learning
    - Computer Vision (OpenCV)
    - 3D Graphics Programming
    - NLP & Transformers
  proficient:
    - Cloud Infrastructure (AWS/GCP/Azure)
    - MLOps & Model Deployment
    - Distributed Training
    - A/B Testing & Experimentation

🎓 Research & Publications

ORCID

Research Interests:

  • 🧠 Graph Neural Networks for Structured Prediction
  • 🏃 Human Pose Estimation & Motion Analysis
  • 📚 Curriculum Learning & Training Optimization
  • 🎨 3D Computer Vision & Graphics
  • 🤖 Reinforcement Learning for Robotics

Current Research:

  • Graph Transformer architectures for human pose estimation
  • Topic-modeled curriculum learning for neural network training
  • Real-time 3D motion capture visualization systems

🎯 2025 Goals & Roadmap

Q1 2025 Q2 2025 Q3 2025 Q4 2025
✅ Launch GTransformer 🚧 Publish Research Paper 📝 Conference Submission 🎯 Open Source Release
✅ MocapViewer3D v1.0 🚧 Advanced RL Projects 📝 Production ML Pipeline 🎯 Community Building
🚧 Curriculum Learning 📝 3D Vision Systems 🚧 Industry Collaboration 🎯 Knowledge Sharing

Key Objectives:

  • 🔬 Publish research in top-tier ML/CV conferences
  • 🌟 Contribute to major open-source ML projects
  • 🏗️ Build production-grade ML systems
  • 👥 Mentor aspiring ML engineers
  • 📚 Share knowledge through blogs & tutorials

🤝 Let's Connect & Collaborate

💬 Open to Opportunities In:

Research CollaborationOpen Source ProjectsML Engineering RolesSpeaking Engagements

Portfolio LinkedIn Email ORCID

📫 Get In Touch

def reach_out():
    interests = {
        "collaborate_on": ["Research projects", "Open source ML tools", "Production systems"],
        "discuss_about": ["Computer Vision", "Deep Learning", "3D Graphics", "MLOps"],
        "available_for": ["Technical consulting", "Speaking", "Mentoring", "Code review"]
    }
    
    contact = {
        "email": "reiyo1113@gmail.com",
        "linkedin": "linkedin.com/in/reiyo06",
        "portfolio": "oreiyo.space"
    }
    
    return "Let's build something amazing together! 🚀"

print(reach_out())

🐍 Contribution Snake

github contribution grid snake animation

💭 Random Dev Quote


⭐️ From RyoK3N with 💜

"The best way to predict the future is to invent it." - Alan Kay

Made with Love Open Source

Pinned Loading

  1. coreml-keras3 coreml-keras3 Public

    Jupyter Notebook

  2. MocapViewer3D MocapViewer3D Public

    An interactive 3D/2D motion capture visualization tool that allows real-time manipulation of camera perspectives and skeleton pose viewing. This tool provides simultaneous 3D and 2D projective view…

    Python

  3. GTransformer GTransformer Public

    Python 1

  4. 2DPoseEstimation 2DPoseEstimation Public

    Python

  5. AI-Projects AI-Projects Public

    Jupyter Notebook

  6. Topic-Modeled-Curriculum-Learning-for-Better-Neural-Network-Training Topic-Modeled-Curriculum-Learning-for-Better-Neural-Network-Training Public

    Jupyter Notebook