Worked on the OpenHUTB/nn repository to deliver core features that enhanced experiment reproducibility, automation, and visualization for machine learning tutorials. Consolidated improvements across scripts for GMM, SVM, softmax, TensorFlow 2.0, and Learn2Carry, introducing environment variable configurability, random seed management, and automated JSON report exports. Stabilized test tooling and updated documentation to support consistent experimentation and faster onboarding. Enhanced the RBM training visualization script by adding new command-line parameters, demo data generation, and improved output handling. Utilized Python, NumPy, and TensorFlow to streamline data analysis, processing, and visualization, focusing on reliability and usability for stakeholders and collaborators.