During July 2025, Nachal Sa developed a Deep Learning Script Suite for the se-sac/sesac-01 repository, enabling end-to-end machine learning experimentation on MNIST, Fashion-MNIST, and transfer learning tasks. Using Python, TensorFlow, and Keras, Nachal implemented reusable scripts for model definition, training, evaluation, and prediction, supporting both ANN and CNN architectures. The suite expanded to include transfer learning workflows for pneumonia detection and flower classification, incorporating data augmentation and image classification techniques. This work established a modular pipeline that reduces friction in experimentation and accelerates feature development, providing a solid foundation for rapid prototyping and AI-enabled product enhancements.