This repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials.
The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the repository enables users to prototype experimental models and implement fundamental algorithms using standard industry frameworks.
The materials cover the core mechanics of tensor-based data flow, automatic differentiation, and computational graph execution. These examples illustrate how to manage model state and optimize mathematical structures for hardware acceleration, providing a practical guide for those learning to build and train models within the framework.
