Problem library
ML building blocks, one implementation at a time.
Practice the functions, model classes, and training loops inside production ML systems.
Binary Cross-Entropy
Implement stable binary cross entropy with optional weights.
Softmax Cross-Entropy
Implement Softmax Cross-Entropy with production-minded edge case handling.
Pairwise Ranking Loss
Implement Pairwise Ranking Loss with production-minded edge case handling.
Hinge Loss
Implement Hinge Loss with production-minded edge case handling.
Focal Loss
Implement Focal Loss with production-minded edge case handling.
Sample Weighting
Implement Sample Weighting with production-minded edge case handling.
Negative Sampling
Implement Negative Sampling with production-minded edge case handling.
IPS Weighting
Implement IPS Weighting with production-minded edge case handling.
PyTorch Training Step
Run one PyTorch forward, MSE loss, backward, optimizer step, and updated prediction pass.