Difference between revisions of "Programming/Deep Learning"
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Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. | Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. | ||
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| + | There is a massive amount of possible applications where Deep Learning can be deployed, these include: | ||
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| + | * Automatic speech recognition | ||
| + | * Image recognition | ||
| + | * Visual art processing | ||
| + | * Natural language processing | ||
| + | * Drug discovery and toxicology | ||
| + | * Customer relationship management | ||
| + | * Recommendation systems | ||
| + | * Bioinformatics | ||
| + | * Image restoration | ||
| + | * Financial fraud detection | ||
| + | * Military | ||
== Further Information == | == Further Information == | ||
Revision as of 11:29, 21 November 2018
Deep Learning
Introduction
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
There is a massive amount of possible applications where Deep Learning can be deployed, these include:
- Automatic speech recognition
- Image recognition
- Visual art processing
- Natural language processing
- Drug discovery and toxicology
- Customer relationship management
- Recommendation systems
- Bioinformatics
- Image restoration
- Financial fraud detection
- Military
Further Information
|