How does Deep Learning differ from traditional Machine Learning?

While both are subsets of AI, traditional machine learning algorithms are often linear, whereas deep learning uses neural networks with many layers which allows it to learn directly from data without relying on handcrafted features.

Why is it called "Deep" Learning?

The term "deep" refers to the use of multiple layers in the neural network. Each layer processes the input from the previous layer, refines it, and passes it to the next layer. This depth allows for complex patterns and representations to be learned.

What is Deep Learning?

A type of machine learning that uses neural networks with many layers.

More about Deep Learning

Deep Learning is a subfield of machine learning that is inspired by the structure and function of the brain, specifically the neural network. It involves using multi-layered neural networks to analyze various factors of data. Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention.

Applications of deep learning include voice and image recognition, medical diagnosis, financial fraud detection, and many types of classification.

Frequently Asked Questions

: While both are subsets of AI, traditional machine learning algorithms are often linear, whereas deep learning uses neural networks with many layers which allows it to learn directly from data without relying on handcrafted features.
: The term "deep" refers to the use of multiple layers in the neural network. Each layer processes the input from the previous layer, refines it, and passes it to the next layer. This depth allows for complex patterns and representations to be learned.

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