(26th-October-2020)
• Deep Learning is now of the hottest trends in Artificial Intelligence and Machine Learning, with daily reports of amazing new achievements, like doing better than humans on IQ test.
• 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)
• 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. Here is 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. Here is a quick start tutorial to walk you through some basic ideas on Pylearn2.
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• 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.
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• 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 in Torch7 paper that “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.
• OverFeat is a feature extractor trained on the ImageNet dataset with Torch7 and also easy to start with.
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• 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/CuDNN supports all the mainstream softwares such as Caffe, Torch and Theano and is very easy to enable.
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• 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.”
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