Computer Science – 18.1 Artificial Intelligence (AI) | e-Consult

Advantages of Deep Learning over Traditional Machine Learning:

• Automatic Feature Extraction: Deep learning models can automatically learn relevant features from raw data, reducing the need for manual feature engineering. This is a significant advantage when dealing with complex data like images, audio, or text.
• High Accuracy: Deep learning models often achieve higher accuracy than traditional machine learning models, especially on complex tasks. This is due to their ability to learn hierarchical representations of data.
• Scalability: Deep learning models can benefit from increased data availability, leading to improved performance as the amount of training data grows.

Disadvantages of Deep Learning compared to Traditional Machine Learning:

• Data Requirements: Deep learning models typically require a large amount of labeled data to train effectively. This can be a major limitation when data is scarce.
• Computational Cost: Training deep learning models can be computationally expensive, requiring powerful hardware (e.g., GPUs) and significant training time.
• Lack of Interpretability: Deep learning models are often considered "black boxes" because it can be difficult to understand how they make their decisions. This lack of interpretability can be a concern in applications where transparency is important.

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