## Problem You are given a machine learning training script with several embedded bugs. Your task is to identify and fix them. **Bug hunt — find at least 4 issues in this pseudocode:** ```python def train(model, X_train, y_train, X_test, y_test): # Bug 1: Normalization uses test stats mean = X_test.mean(axis=0) std = X_test.std(axis=0) X_train = (X_train - mean) / std X_test = (X_test - mean) / std # Bug 2: Shuffle happens after train/test split (data already split above) shuffle_in_place(X_train, y_train) # should be before split for epoch in range(100): loss = model.forward(X_train, y_train) # Bug 3: Gradient not zeroed before backward model.backward(loss) model.step(lr=0.01) # Bug 4: Evaluating on training data, not test data acc = model.evaluate(X_train, y_train) print(f"Epoch {epoch}: acc={acc}") return model ``` ## Follow-ups 1. Why does normalizing using test statistics cause data leakage? 2. What is the effect of not zeroing gradients — in which framework (PyTorch/TF) does this matter most? 3. How would you structure a training loop to prevent these classes of bugs systematically? 4. What automated checks (e.g., assertions, dataset auditing) would you add before training starts?

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ML Bug Squash: Identify and Fix Common Bugs in a Training Pipeline

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Problem You are given a machine learning training script with several embedded bugs. Your task is to identify and fix them. Bug hunt — find at least 4 issues in this pseudocode: Follow-ups Why does normalizing using test statistics cause data leakage? What is the effect of not zeroing gradients — in which framework (PyTorch/TF) does this matter most? How would you structure a training loop to prevent these classes of bugs systematically? What automated checks (e.g., assertions, dataset auditing)…

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ML Bug Squash: Identify and Fix Common Bugs in a Training Pipeline

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