Reading list: What makes deep learning better than traditional ML?
Deep learning is superior to traditional ML in several ways:
Handles Large Data: Deep learning excels with vast amounts of unstructured data (images, text, audio), while traditional ML struggles with this without heavy preprocessing.

Automatic Feature Extraction: Deep learning automatically identifies important features from raw data, unlike traditional ML which requires manual feature engineering.

Better Accuracy: Deep learning models generally outperform traditional ML in tasks like image recognition, speech recognition, and NLP.

Improved Generalization: Deep learning models tend to generalize better to new data, while traditional ML can struggle without proper tuning.
Scalability: Deep learning models improve with larger datasets, whereas traditional ML may plateau.

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