
Developed core machine learning and computer vision features for the pskcci/DX-01 repository, focusing on educational tooling and real-time demo applications. Delivered a consolidated ML coursework suite with NumPy-based data manipulation, image processing, and neural network models including Perceptron, CNN, and RNN for stock forecasting. Established documentation scaffolding to support classroom onboarding and reproducibility. Built OpenVINO-powered motion recognition and U2NET-based background removal apps enabling gesture-driven user experiences and frame capture. Integrated Python-driven hardware demos for LED control and factory motion detection, emphasizing modular code and maintainable architecture. Prioritized reliability, clear documentation, and scalable solutions across both software and hardware domains.
Month 2024-12: Delivered CV-driven features and hardware-in-loop demos in pskcci/DX-01, focusing on real-time gesture-based UX and end-to-end demo capability. Key outcomes include an OpenVINO-based motion recognition app with gesture-triggered background changes and U2NET-based background removal with frame capture/review, plus Class02 homework scripts for LED control and factory motion-detection simulation. These efforts enable interactive demos, faster validation, and a path toward reusable components across CV and hardware integrations.
Month 2024-12: Delivered CV-driven features and hardware-in-loop demos in pskcci/DX-01, focusing on real-time gesture-based UX and end-to-end demo capability. Key outcomes include an OpenVINO-based motion recognition app with gesture-triggered background changes and U2NET-based background removal with frame capture/review, plus Class02 homework scripts for LED control and factory motion-detection simulation. These efforts enable interactive demos, faster validation, and a path toward reusable components across CV and hardware integrations.
2024-11 monthly performance for pskcci/DX-01 focused on delivering core ML coursework tooling and foundational documentation to support classroom usage and onboarding. Key features include a consolidated ML Coursework Suite featuring NumPy data manipulation scripts, image processing, and multiple neural network models (Perceptron, ANN, CNNs) plus an RNN-based stock price forecasting component. Documentation scaffolding was established for class packages and mini-projects, including placeholder READMEs and updated participant lists. No critical bugs were reported; the work emphasizes reliability, reproducibility, and scalable educational tooling.
2024-11 monthly performance for pskcci/DX-01 focused on delivering core ML coursework tooling and foundational documentation to support classroom usage and onboarding. Key features include a consolidated ML Coursework Suite featuring NumPy data manipulation scripts, image processing, and multiple neural network models (Perceptron, ANN, CNNs) plus an RNN-based stock price forecasting component. Documentation scaffolding was established for class packages and mini-projects, including placeholder READMEs and updated participant lists. No critical bugs were reported; the work emphasizes reliability, reproducibility, and scalable educational tooling.

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