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LanguageDevelopement InfoAdvantageDisadvantageNotes
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TensorflowPythonGoogle
written in C++
1. Can be used in python
2. Use graph to abstract the computations
3. Tensor board is a good visulization
1. Lower the speed when connect
python to lower level
2. Lack of pre-trained models
3. not fully open source		Deep learning with extendible graphs
tensorflow.org
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CNTK
(cognitive toolkit)		PythonMicrosoft
written in C++		1. C++, C#, JAVA, PYTHON update
2. Flexibility
3. Support distribute computing		1. lower levely, it is Network Definition Language
2. no visulization		Open-source deep learning library
github.com/Microsoft/CNTK
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TheanoPythonwritten in Python1. Optimization between CPU / GPU
2. Fast computation for arrays
1. focus on low level implementation
2. need to combine with higher level API for abstract
mathematical expressions involving
multi-dimensional arrays
deeplearning.net/software/theano/
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Caffe2Python
Matlab		Facebook
written in C++		1. Can be used wih Python and Matlab
2. Train a model without even coding		1. Lack of recurrent nets
2. Hard to develop new networks		deep learning framework
for rapid development
research.fb.com/downloads/caffe2/
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KerasPythonwritten in Python1. Highly extendible
2. CPU GPU bettwer combination
3. Can use TF, CNTK, or Theano		1. As a high level API,
backend needs to be considered		open source deel learning library
keras.io
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TorchLuawritten in C1. Flexibility
2. High speed
3. Pre-trained models		1. Hard to learn
2. Lack of plug and play codes
3. Based on Lua		opence source machine learning library
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Accord .NETC#written in C#1. Good documentation
2. Good visulization		1. slower than tensorflowML, CV, Statistics focus on .net
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Spark MLlibScalawritten in Scala1. speed up for big data
2. support multiple languages		1. Hard to learn
2. Plug and play only for Hadoop		Extendible ML library
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