Misplaced Pages

Talk:Comparison of deep learning software/Resources: Difference between revisions

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.
< Talk:Comparison of deep learning software Browse history interactively← Previous editContent deleted Content addedVisualWikitext
Revision as of 10:27, 24 December 2017 editPacket0 (talk | contribs)29 edits Made it easier to distinguish existing and non-existing pages← Previous edit Latest revision as of 01:55, 10 March 2018 edit undoHuon (talk | contribs)Administrators51,324 edits uncategorize 
(9 intermediate revisions by 7 users not shown)
Line 1: Line 1:
{{Underlinked|date=March 2016}}

This page lists resources that can be useful to the ] page. This page lists resources that can be useful to the ] page.


==Deep learning software not yet covered== ==Deep learning software not yet covered==


{{Expand list|date=April 2017}}


This is a list of deep learning software that is not listed on the ] because they lack a Misplaced Pages article. If you would like to see any of these pieces of software listed there, you are welcome to create a Misplaced Pages article for it. This is a list of deep learning software that is not listed on the ] because they lack a Misplaced Pages article. If you would like to see any of these pieces of software listed there, you are welcome to create a Misplaced Pages article for it.


* ] – Javascript neural networks * ] – Javascript neural networks
* ] – Theano framework for building and training neural networks * Blocks – Theano framework for building and training neural networks
* ] – Deep learning framework built on ], developed by ] in cooperation with ], ], ], ], and ]<ref>https://caffe2.ai/blog/2017/04/18/caffe2-open-source-announcement.html</ref> * ] – Deep learning framework built on ], developed by ] in cooperation with ], ], ], ], and ]<ref>https://caffe2.ai/blog/2017/04/18/caffe2-open-source-announcement.html</ref>
* ] – Scalable deep learning package running Caffe on ] and ] clusters with ] communication * ] – Scalable deep learning package running Caffe on ] and ] clusters with ] communication
* ] – Flexible neural network framework, adopting a "Define-by-run" scheme where the actual forward computation defines the network * ] – Flexible neural network framework, adopting a "Define-by-run" scheme where the actual forward computation defines the network
* ] – Deep learning framework using GPU and FPGA-based accelerators * ] – Deep learning framework using GPU and FPGA-based accelerators
* ] – Javascript library for training deep learning models entirely in a web browser * ] – Javascript library for training deep learning models entirely in a web browser
* ] – Theano-based deep learning toolbox for neuroimaging * ] – Theano-based deep learning toolbox for neuroimaging
* ] – Optimized deep learning computation primitives implemented in CUDA * ] – Optimized deep learning computation primitives implemented in CUDA
* ] – CUDA-accelerated toolkit for deep Long Short-Term Memory (LSTM) RNN architectures supporting large data sets not fitting into main memory. * ] – CUDA-accelerated toolkit for deep Long Short-Term Memory (LSTM) RNN architectures supporting large data sets not fitting into main memory.
Line 22: Line 19:
* ] – OpenCL library to train deep convolutional networks, with APIs for C++, Python and the command line * ] – OpenCL library to train deep convolutional networks, with APIs for C++, Python and the command line
* ] – Hardware-accelerated deep learning library for the web browser * ] – Hardware-accelerated deep learning library for the web browser
* ] – Open source deep learning framework for iOS, OS X and tvOS<ref>http://arxiv.org/pdf/1605.04614v1.pdf</ref> * ] – Open source deep learning framework for iOS, OS X and tvOS<ref>https://arxiv.org/pdf/1605.04614v1.pdf</ref>
* ] – Matlab/Octave toolbox for deep learning (deprecated) * ] – Matlab/Octave toolbox for deep learning (deprecated)
* ] – Software accelerator for deep learning execution aimed towards mobile devices * ] – Software accelerator for deep learning execution aimed towards mobile devices
Line 31: Line 28:
* ] – ]® Deep Learning Framework; supports OpenCL (deprecated) * ] – ]® Deep Learning Framework; supports OpenCL (deprecated)
* Intel ] (Intel MKL),<ref>https://software.intel.com/en-us/articles/introducing-dnn-primitives-in-intelr-mkl</ref> library of optimized math routines, including optimized deep learning computation primitives * Intel ] (Intel MKL),<ref>https://software.intel.com/en-us/articles/introducing-dnn-primitives-in-intelr-mkl</ref> library of optimized math routines, including optimized deep learning computation primitives
* ] – Deep Learning library for Theano and TensorFlow * Lasagne – Lightweight library to build and train neural networks in Theano
⚫ * Leaf – "The Hacker's Machine Learning Engine"; supports OpenCL (official development suspended<ref>{{cite web|author=Michael Hirn|url=https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.u5yveb1le|title=Tensorflow wins|date=9 May 2016|accessdate=17 August 2016|quote=... I will suspend the development of Leaf and focus on new ventures.}}</ref>)
* ] – Lightweight library to build and train neural networks in Theano
⚫ * ] – MATLAB-based environment for deep learning
⚫ * ] – "The Hacker's Machine Learning Engine"; supports OpenCL (official development suspended<ref>{{cite web|author=Michael Hirn|url=https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.u5yveb1le|title=Tensorflow wins|date=9 May 2016|accessdate=17 August 2016|quote=... I will suspend the development of Leaf and focus on new ventures.}}</ref>)
⚫ * ] – MATLAB-based environment for deep learning
* ] – CNNs for MATLAB
* ] – Distributed TensorFlow with MPI by ] * ] – Distributed TensorFlow with MPI by ]
* ] – Deep learning framework for ], inspired by Caffe * Mocha – Deep learning framework for ], inspired by Caffe
* ] – Nervana's Python based Deep Learning framework * neon – Nervana's Python based Deep Learning framework
⚫ * Purine – Bi-graph based deep learning framework<ref>https://arxiv.org/abs/1412.6249</ref>
* ] – MATLAB toolbox for neural network creation, training and simulation
* ] – "PArallel Distributed Deep LEarning", deep learning platform
⚫ * ] – Bi-graph based deep learning framework<ref>https://arxiv.org/abs/1412.6249</ref>
* ] – Machine learning library mainly built on top of Theano * ] – Machine learning library mainly built on top of Theano
* ] - Python based implementation of Torch API, allows for dynamic graph construction
* ] – Multi-layer perceptrons as a wrapper for Pylearn2 * ] – Multi-layer perceptrons as a wrapper for Pylearn2
* ] – Scikit-learn compatible tools using theano * ] – Scikit-learn compatible tools using theano
Line 55: Line 47:
* ] – Header only, dependency-free deep learning framework in C++11 * ] – Header only, dependency-free deep learning framework in C++11
* ] – Torch framework providing a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming<ref>https://code.facebook.com/posts/580706092103929</ref><ref>{{cite web|author1=Ronan Collobert|author2=Laurens van der Maaten|author3=Armand Joulin|title=Torchnet: An Open-Source Platform for (Deep) Learning Research|url=https://lvdmaaten.github.io/publications/papers/Torchnet_2016.pdf|publisher=Facebook AI Research|accessdate=24 June 2016}}</ref> * ] – Torch framework providing a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming<ref>https://code.facebook.com/posts/580706092103929</ref><ref>{{cite web|author1=Ronan Collobert|author2=Laurens van der Maaten|author3=Armand Joulin|title=Torchnet: An Open-Source Platform for (Deep) Learning Research|url=https://lvdmaaten.github.io/publications/papers/Torchnet_2016.pdf|publisher=Facebook AI Research|accessdate=24 June 2016}}</ref>
* ] – Distributed machine learning platform by ] * Veles – Distributed machine learning platform by ]


==Related software== ==Related software==
* ]<ref>http://arxiv.org/abs/1506.06579</ref><ref>http://yosinski.com/deepvis</ref> – Software tool for "probing" DNNs by feeding them image data and watching the reaction of every neuron, and for visualizing what a specific neuron "wants to see the most" * ]<ref>https://arxiv.org/abs/1506.06579</ref><ref>http://yosinski.com/deepvis</ref> – Software tool for "probing" DNNs by feeding them image data and watching the reaction of every neuron, and for visualizing what a specific neuron "wants to see the most"
* ] – A visual analysis tool for recurrent neural networks * ] – A visual analysis tool for recurrent neural networks
* ] – Simple, realtime visualization of neural network training performance * ] – Simple, realtime visualization of neural network training performance
Line 85: Line 77:
* *
* *

{{DEFAULTSORT:Comparison of deep learning software Resources}}
]

Latest revision as of 01:55, 10 March 2018

This page lists resources that can be useful to the Comparison of deep learning software page.

Deep learning software not yet covered

This is a list of deep learning software that is not listed on the main page because they lack a Misplaced Pages article. If you would like to see any of these pieces of software listed there, you are welcome to create a Misplaced Pages article for it.

  • adnn – Javascript neural networks
  • Blocks – Theano framework for building and training neural networks
  • Caffe2 – Deep learning framework built on Caffe, developed by Facebook in cooperation with NVIDIA, Qualcomm, Intel, Amazon, and Microsoft
  • CaffeOnSpark – Scalable deep learning package running Caffe on Spark and Hadoop clusters with peer-to-peer communication
  • Chainer – Flexible neural network framework, adopting a "Define-by-run" scheme where the actual forward computation defines the network
  • CNNLab – Deep learning framework using GPU and FPGA-based accelerators
  • ConvNetJS – Javascript library for training deep learning models entirely in a web browser
  • Cortex – Theano-based deep learning toolbox for neuroimaging
  • cuDNN – Optimized deep learning computation primitives implemented in CUDA
  • CURRENNT – CUDA-accelerated toolkit for deep Long Short-Term Memory (LSTM) RNN architectures supporting large data sets not fitting into main memory.
  • Darknet - Darknet is an open source neural network framework written in C and CUDA, and supports CPU and GPU computation.
  • DeepCL – OpenCL library to train deep convolutional networks, with APIs for C++, Python and the command line
  • deeplearn.js – Hardware-accelerated deep learning library for the web browser
  • DeepLearningKit – Open source deep learning framework for iOS, OS X and tvOS
  • DeepLearnToolbox – Matlab/Octave toolbox for deep learning (deprecated)
  • DeepX – Software accelerator for deep learning execution aimed towards mobile devices
  • deepy – Extensible deep learning framework based on Theano
  • DSSTNE (Deep Scalable Sparse Tensor Network Engine) – Amazon developed library for building deep learning models
  • Faster RNNLM (HS/NCE) toolkit – An rnnlm implementation for training on huge datasets and very large vocabularies and usage in real-world ASR and MT problems
  • GNU Gneural Network – GNU package which implements a programmable neural network
  • IDLF – Intel® Deep Learning Framework; supports OpenCL (deprecated)
  • Intel Math Kernel Library (Intel MKL), library of optimized math routines, including optimized deep learning computation primitives
  • Lasagne – Lightweight library to build and train neural networks in Theano
  • Leaf – "The Hacker's Machine Learning Engine"; supports OpenCL (official development suspended)
  • LightNet – MATLAB-based environment for deep learning
  • MaTEx – Distributed TensorFlow with MPI by PNNL
  • Mocha – Deep learning framework for Julia, inspired by Caffe
  • neon – Nervana's Python based Deep Learning framework
  • Purine – Bi-graph based deep learning framework
  • Pylearn2 – Machine learning library mainly built on top of Theano
  • scikit-neuralnetwork – Multi-layer perceptrons as a wrapper for Pylearn2
  • sklearn-theano – Scikit-learn compatible tools using theano
  • Tensor Builder – Lightweight extensible library for easy creation of deep neural networks using functions from "any Tensor-based library" (requires TensorFlow) through an API based on the Builder Pattern
  • TensorGraph – Framework for building any models based on TensorFlow
  • TensorFire – Neural networks framework for the web browser, accelerated by WebGL
  • TF Learn (Scikit Flow) – Simplified interface for TensorFlow
  • TF-Slim – High level library to define complex models in TensorFlow
  • TFLearn – Deep learning library featuring a higher-level API for TensorFlow
  • Theano-Lights – Deep learning research framework based on Theano
  • tiny-dnn – Header only, dependency-free deep learning framework in C++11
  • torchnet – Torch framework providing a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming
  • Veles – Distributed machine learning platform by Samsung

Related software

  • Deep Visualization Toolbox – Software tool for "probing" DNNs by feeding them image data and watching the reaction of every neuron, and for visualizing what a specific neuron "wants to see the most"
  • LSTMVis – A visual analysis tool for recurrent neural networks
  • pastalog – Simple, realtime visualization of neural network training performance

References

  1. https://caffe2.ai/blog/2017/04/18/caffe2-open-source-announcement.html
  2. https://arxiv.org/pdf/1605.04614v1.pdf
  3. https://software.intel.com/en-us/articles/introducing-dnn-primitives-in-intelr-mkl
  4. Michael Hirn (9 May 2016). "Tensorflow wins". Retrieved 17 August 2016. ... I will suspend the development of Leaf and focus on new ventures.
  5. https://arxiv.org/abs/1412.6249
  6. https://code.facebook.com/posts/580706092103929
  7. Ronan Collobert; Laurens van der Maaten; Armand Joulin. "Torchnet: An Open-Source Platform for (Deep) Learning Research" (PDF). Facebook AI Research. Retrieved 24 June 2016.
  8. https://arxiv.org/abs/1506.06579
  9. http://yosinski.com/deepvis

External links