In?2015 KDnuggets Software Poll, a new category for Deep Learning Tools was added, with most popular tools in that poll listed below.
Pylearn2 (55 users)
Theano (50)
Caffe (29)
Torch (27)
Cuda-convnet (17)
Deeplearning4j (12)
Other Deep Learning Tools (106)
I haven’t used all of them, so this is a brief summary of these popular tools based on their homepages and tutorials.
Theano and Pylearn2 are both developed at University of Montreal with most developers in the LISA group led by Yoshua Bengio. Theano?is a Python library, and you can also consider it as a mathematical expression compiler. It is good for making algorithms from scratch.Hereis an intuitive example of Theano training.
If we want to use standard algorithms, we can write Pylearn2 plugins as Theano expressions, and Theano will optimize and stabilize the expressions. It includes all things needed for multilayer perceptron/RBM/Stacked Denoting Autoencoder/ConvNets.Hereis a quick start tutorial to walk you through some basic ideas on Pylearn2.
Caffe is developed by the Berkeley Vision and Learning Center, created by Yangqing Jia and led by Evan Shelhamer. It is a fast and readable implementation of ConvNets in C++. As shown on its official page,?Caffe can process?over 60M images per day?with a single NVIDIA K40 GPU with AlexNet. It can be used like a toolkit for image classification, while not for other deep learning application such as text or speech.
Torch is written in Lua, and used at NYU, Facebook AI lab and Google DeepMind. It claims to provide a MATLAB-like environment for machine learning algorithms. Why did they choose Lua/LuaJIT instead of the more popular Python? They said inTorch7 paperthat “Lua is easily to be integrated with C so within a few hours’ work, any C or C++ library can become a Lua library.” With Lua written in pure ANSI C, it can be easily compiled for arbitrary targets.
Cuda:
There is no doubt that GPU accelerates deep learning researches these days. News about GPU especially Nvidia Cuda is all over the Internet.Cuda-convnet/CuDNNsupports all the mainstream softwares such as Caffe, Torch and Theano and is very easy to enable.
Unlike the above packages, Deeplearning4j is designed to be used in business environments, rather than as a research tool. As on its introduction, DL4J is a “Java-based, industry-focused, commercially supported, distributed deep-learning framework.”
Comparison
These tools seem to be in a friendly competition of speed and ease of use.
Caffe developers say that “Caffe?is the fastest convnet implementation available.”
Torch7 is proved to be faster than Theano on most benchmarks as shown inTorch7 paper.
Soumith gave hisconvnet benchmarksof all public open-source implementations.
A comparison table of some popular deep learning tools is listed in theCaffe paper.
There isa thread on redditabout “best framework for deep neural nets”. DL4J also givesDL4J vs. Torch vs. Theano vs. Caffeon its website.
Related:
R leads RapidMiner, Python catches up, Big Data tools grow, Spark ignites
Where to Learn Deep Learning – Courses, Tutorials, Software
CuDNN – A new library for Deep Learning
What is your favorite Deep Learning package?
