Deep Learning Frameworks
The purpose of this post is to write down what I know about these frameworks, as I learn more, I will keep on updating with more information. These are the deep learning frameworks that I have used or am using:
- Both C++ and Python
- Released by Google
Its a python library that allows one to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Developed at University of Montreal.
Some important and beneficiary features of Theano are:
- Tight integration with NumPy
- Use numpy ndarray in Theano-compiled functions.
- Hence Theano has been powered for large-scale computationally intensive investigations as it is also approachable enough to be used in the classroom.
- The package contains a number of utility modules that are helpful with machine learning tasks.
- Keras, Pylearn2 are some other libraries which are build on Theano.
- Have a state of the art model for RNN and LSTM
- Supports automatic gradient differentiation which is considered one of the most important points about it.
- Lower level abstraction offered by working with symbolic functions mean that more freedom with what we want is possible
- This might be one of the reasons why it may seem complicated, there are no ready to run models, everything must be done on our own.
- I think the underlying architecture of Theano converts everything to a computational graph which makes fast calculations and calculations that can be shared easily among different parts of a process.
- Berkeley Vision and Learning Center developed it
- Pretrained models are available for MNIST and other datasets which makes their use easy and highly newbie friendly
- Extremely portable
- Fast, Caffe is said to have the fastest implementation of CNN
- Caffe installation on OS X is slightly difficult. It was so much more easier on Ubuntu 14.04
- Can be easily configured to use GPU’s using flags
- Python library
- Implementation of Word2Vec and Doc2Vec which is widely used
- Easy installation
- A Java and Scala library.
- Mainly a business framework
- Brings a scala-like thinking
- Easy installation
- I am a newbie at it because of not much knowledge about Lua. Torch is written in Lua.
- Written in Lua which is lightweight and easy to write wrappers of ultra fast C and C++ for Lua.
- State of the art convolutional network models
- Support by GPU acceleration
- CuDNN supports other frameworks and libraries
- NVIDIA GPU’s only
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