What is the key difference between deep learning and machine learning?

Machine learning relies on structured data and predefined rules. Deep learning uses neural networks to learn patterns from unstructured data. Deep learning mimics human decision-making, making it more effective for complex tasks like image and speech recognition.

When should I use deep learning instead of traditional machine learning?

Deep learning is ideal when handling large-scale, unstructured data like images, videos, and natural language. It excels in tasks requiring high levels of abstraction, such as facial recognition, self-driving cars, and predictive analytics.

How does deep learning improve image and speech recognition?

Deep learning models process raw data using convolutional and recurrent neural networks, automatically detecting intricate patterns in images and speech. This enables real-time facial recognition, voice assistants, and accurate medical imaging analysis with minimal human intervention.

What are the major challenges in implementing deep learning?

Challenges include the need for massive datasets, high computational power, and long training times. Deep learning models can also be opaque, making explain ability and bias detection difficult.

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