Deep learning is superior to traditional ML in several ways:

1. Handles Large Data: Deep learning excels with vast amounts of unstructured data (images, text, audio), while traditional ML struggles with this without heavy preprocessing.
    
2. Automatic Feature Extraction: Deep learning automatically identifies important features from raw data, unlike traditional ML which requires manual feature engineering.
    
3. Better Accuracy: Deep learning models generally outperform traditional ML in tasks like image recognition, speech recognition, and NLP.
    
4. Improved Generalization: Deep learning models tend to generalize better to new data, while traditional ML can struggle without proper tuning.
    
5. Scalability: Deep learning models improve with larger datasets, whereas traditional ML may plateau.
    
6. End-to-End Learning: Deep learning simplifies the process by learning directly from input to output, unlike traditional ML which requires multiple stages.
    
7. Versatility: Deep learning is ideal for complex tasks, like autonomous driving and real-time recognition, that traditional ML can't handle as effectively.
    

In summary, deep learning is better for complex, large-scale tasks, while traditional ML works well for simpler, structured problems.