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    Python (Pyodide)
    features (X), labels (y), "optimizer"(str), "scheduler"(str), "device"(str)
    [ ]:
    def build_training_pipeline(*args, **kwargs):
    """Create train/validation loaders and initialize model, optimizer,
    scheduler, criterion, and mixed-precision training config.
    Args:
    config: dict
    train_df: pandas.DataFrame
    val_df: pandas.DataFrame
    Returns:
    model: torch.nn.Module
    optimizer: torch.optim.Optimizer
    scheduler: torch.optim.lr_scheduler._LRScheduler
    """
    # Your code for this task here
    # Reuse the same notebook structure and modify signature if needed
    pass
    Task 3: Implement deep learning training loop with early stopping, gradient clipping, and validation tracking
    [ ]:
    def train_model(model, train_loader, val_loader, optimizer, scheduler, criterion, scaler, device):
    """Train a neural network and track train_loss, val_loss, val_f1,
    learning rate schedule, and best checkpoint state.
    Args:
    model: torch.nn.Module
    train_loader: torch.utils.data.DataLoader
    val_loader: torch.utils.data.DataLoader
    Returns:
    history: dict[str, list[float]]
    best_state_dict: dict[str, torch.Tensor]
    """
    # Train for N epochs with mixed precision
    # Apply gradient clipping and patience-based early stopping
    pass
    [ ]:
    def evaluate_model(model, dataloader, criterion, device):
    """Compute validation loss, accuracy, F1, AUROC, and confusion matrix."""
    # Return both aggregate metrics and raw predictions
    pass
    Epoch 12/30
    train_loss: 0.1842 | val_loss: 0.2217 | val_f1: 0.912 | lr: 0.0003
    early_stopping_counter: 2/5 | gradient_clip_norm: 1.0

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    Test takers working in notebooks, containers, or realistic AI workflows that mirror actual on-the-job execution.

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    01

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    03

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    Built for Teams Developing and Hiring AI Engineers

    Codeaid helps organizations run coding assessments and online coding tests for AI and ML engineering roles — from initial screening to final technical evaluation. Whether you're hiring machine learning engineers or upskilling your existing team, Codeaid gives you the tools to evaluate real skills, not theoretical knowledge.

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    Trusted by Teams Building AI Engineering Talent

    See how teams use Codeaid to evaluate AI and ML engineering talent more effectively.

    “After screening hundreds of internal and external candidates for ML/AI roles, I can tell that it's genuinely difficult to tell who can handle real-world work. Codeaid made that easier — I can have the AI Interviewer generate a custom assessment in minutes, or simply use one of their ready-made templates. The tests are rigorous, and the evaluations are so accurate and detailed that we actually trust them.”

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    Anastasiia P.

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