Difference between revisions of "Programming/Deep Learning"

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* Recommendation systems
 
* Recommendation systems
 
* Bioinformatics
 
* Bioinformatics
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* Health diagnostics
 
* Image restoration
 
* Image restoration
 
* Financial fraud detection
 
* Financial fraud detection
* Military
+
 
 +
===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 ==
 
== 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

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