```html
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">

  <title>Neural Network Flowchart</title>

  <!-- Mermaid -->
  <script type="module">
    import mermaid from 'https://cdn.jsdelivr.net/npm/mermaid@11/dist/mermaid.esm.min.mjs';

    mermaid.initialize({
      startOnLoad: true,
      securityLevel: 'loose'
    });
  </script>
</head>

<body>

  <h1>Neural Network Architecture</h1>

  <div class="mermaid">
    flowchart TD
      subgraph group_interfaces["Entry Points"]
        node_train_cli["Training CLI<br>Python CLI<br>[train.py]"]
        node_evaluate_cli["Evaluation CLI<br>Python CLI<br>[evaluate.py]"]
        node_predict_cli["Prediction CLI<br>Python CLI<br>[predict.py]"]
        node_quickstart["Offline smoke demo<br>example API<br>[quickstart.py]"]
      end

      subgraph group_core["Learning Core"]
        node_model["NeuralNetwork<br>stateful model<br>[model.py]"]
        node_activations["Activations<br>math module<br>[activations.py]"]
        node_losses["Losses<br>math module<br>[losses.py]"]
        node_training["Training loop<br>orchestrator<br>[training.py]"]
        node_optimizers["Optimizers<br>update policies<br>[optimizers.py]"]
        node_package_api["Package API<br>public exports<br>[__init__.py]"]
        node_gradient_checks{{"Gradient checks<br>correctness guard<br>[test_gradients.py]"}}
      end

      subgraph group_data["Data and Reporting"]
        node_data_loader["Data loader<br>ingestion adapter<br>[data_loader.py]"]
        node_mnist_sources[("MNIST sources<br>external/local data")]
        node_metrics["Classification metrics<br>reporting module<br>[metrics.py]"]
        node_visualize["Visualization<br>plotting module<br>[visualize.py]"]
        node_model_artifacts[("Saved models<br>NPZ artifacts")]
        node_results["Plot outputs<br>generated artifacts"]
      end

      node_train_cli -->|"loads data"| node_data_loader
      node_train_cli -->|"starts"| node_training
      node_quickstart -->|"uses API"| node_model
      node_package_api -->|"exports"| node_model
      node_data_loader -->|"acquires"| node_mnist_sources
      node_data_loader -->|"normalized arrays"| node_training
      node_training -->|"forward and backward"| node_model
      node_training -->|"applies updates"| node_optimizers
      node_model -->|"hidden layers"| node_activations
      node_model -->|"cross-entropy and L2"| node_losses
      node_training -->|"validation predictions"| node_metrics
      node_training -->|"training history"| node_visualize
      node_train_cli -->|"saves NPZ"| node_model_artifacts
      node_evaluate_cli -->|"loads NPZ"| node_model_artifacts
      node_predict_cli -->|"loads NPZ"| node_model_artifacts
      node_evaluate_cli -->|"evaluates predictions"| node_metrics
      node_evaluate_cli -->|"renders reports"| node_visualize
      node_predict_cli -->|"renders samples"| node_visualize
      node_visualize -->|"writes plots"| node_results
      node_gradient_checks -.->|"checks gradients"| node_model
      node_gradient_checks -.->|"varies L2 loss"| node_losses

      click node_train_cli "https://github.com/seicq/neural-network-from-scratch/blob/main/scripts/train.py"
      click node_evaluate_cli "https://github.com/seicq/neural-network-from-scratch/blob/main/scripts/evaluate.py"
      click node_predict_cli "https://github.com/seicq/neural-network-from-scratch/blob/main/scripts/predict.py"
      click node_quickstart "https://github.com/seicq/neural-network-from-scratch/blob/main/examples/quickstart.py"
      click node_model "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/model.py"
      click node_activations "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/activations.py"
      click node_losses "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/losses.py"
      click node_training "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/training.py"
      click node_optimizers "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/optimizers.py"
      click node_package_api "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/__init__.py"
      click node_data_loader "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/data_loader.py"
      click node_metrics "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/metrics.py"
      click node_visualize "https://github.com/seicq/neural-network-from-scratch/blob/main/src/neural_network/visualize.py"
      click node_gradient_checks "https://github.com/seicq/neural-network-from-scratch/blob/main/tests/test_gradients.py"

      classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
      classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
      classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
      classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d

      class node_train_cli,node_evaluate_cli,node_predict_cli,node_quickstart toneBlue
      class node_model,node_activations,node_losses,node_training,node_optimizers,node_package_api,node_gradient_checks toneAmber
      class node_data_loader,node_mnist_sources,node_metrics,node_visualize,node_model_artifacts,node_results toneMint
  </div>

</body>
</html>
```