TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations.
Features
- Interactive Jupyter-based coding environment
- Automated grading with correctness and gradient validation
- Curated problem sets covering core PyTorch concepts
- Hints and reference solutions for guided learning
- Progress tracking and performance metrics
- Support for local and cloud-based execution