Pocketgrad

* **Clean, readable Python with NumPy:** Uses clear variable names and straightforward logic. Leverages NumPy for efficient array operations. * **Manual gradient calculation:** Implements a basic autograd engine, manually handling gradient calculations. Could benefit from more sophisticated techniques for larger computations. * **Simple, modular structure:** Code is organized into logical modules (`engine`, `tests`), facilitating testing and maintenance. Unit tests present, but test coverage unknown. * **Room for optimization:** Gradient updates are simple; more efficient algorithms (e.g., Adam, SGD) could improve performance. Extensive use of NumPy provides a good foundation for further vectorization. * **Security concerns:** High number of reported critical security issues necessitates immediate investigation and remediation. Addressing these is paramount before deployment. * **Improve documentation and testing:** While some tests exist, comprehensive unit tests and detailed docstrings are needed for improved maintainability and understanding.