Difference between revisions of "Programming/Deep Learning"
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* Recommendation systems | * Recommendation systems | ||
* Bioinformatics | * Bioinformatics | ||
| + | * Health diagnostics | ||
* Image restoration | * Image restoration | ||
* Financial fraud detection | * Financial fraud detection | ||
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| + | ===Development Environments=== | ||
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| + | There are the following development environments already part of our HPC | ||
| + | |||
| + | * Python 3.5 with Tensorflow (and Keras), and theano. | ||
| + | * C/C++/Fortran with CUDA GPU programming. | ||
| + | * PGI compiler with openACC programming for C and Fortran. | ||
| + | * Matlab with deep learning libraries. | ||
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== Further Information == | == Further Information == | ||
Revision as of 11:44, 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
- Health diagnostics
- Image restoration
- Financial fraud detection
Development Environments
There are the following development environments already part of our HPC
- Python 3.5 with Tensorflow (and Keras), and theano.
- C/C++/Fortran with CUDA GPU programming.
- PGI compiler with openACC programming for C and Fortran.
- Matlab with deep learning libraries.
Further Information
|