Unlike h2oai graphlab supports the use of gpus

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Unlike H2O.ai, Graphlab supports the use of GPUs. **Theano**is a Python library. It was developed by a machine learning group headed by Yoshua Bengio at the University of Montreal. It is more a research platform than a deep learning library. You must perform more work by yourself to generate the models that you want. It does not have any
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deep learning classes within itself Theano - Features Theano allows us to define mathematical expressions as a set of vectors and matrices. This avoids too many for loops in our code and greatly reduces the computation time. Theano is best suited when we are going to build everything from scratch. It just aids in representing our deep net in terms of vectors and matrices. Some of the optimization techniques used by Theano are the use of GPU for computations, arithmetic simplification, constant folding, using memory aliasing to avoid calculation, etc. Popular libraries like Keras, Lasagne, Blocks, and Pylearn2 are built on top of Theano. DeepLearning4J (DL4J) is a Deep Learning framework created in Java and JVM languages for using in commercial deep learning projects. Adam Gibson developed DL4J. DL4J is utilized in business environments on distributed CPUs and GPUs, making it ideal for commercial-grade applications. Features DL4J runs on distributed GPUs and CPUs. It allows us to tune the deep net by selecting values for hyperparameters It supports most of the deep nets like DBN, RBN, CNN, RNTN, autoencoders, Recurrent net and vanilla MLP. It also includes vectorization library called Canova and distributed multi-node map reduce procedure for training the model. Torch is a Lua deep learning framework developed by Koray Kavukcuoglu, Clement Farabet and Ronan Collobert for research and development activities into deep learning algorithms. Torch is written in LuaJit (framework in Lua programming language) with an underlying C implementation. It has also been further contributed by Facebook, Google DeepMind, Twitter and a host of others. Popular applications of Torch are for supervised image problems with Convolutional Neural Networks and agents in more complex domains with deep reinforcement learning. Features Torch allows us to set up, train, and model deep net by configuring its hyperparameters. Fast and efficient GPU support. Provides built-in functions for indexing, transposing, slicing and numeric optimization. Embeddable, with ports to iOS and Android backends. Caffe library was developed by Yangqing Jia at the Berkeley Vision and Learning Center for supervised computer vision problems. It is written in C++ with a Python interface It is mainly suited for machine vision tasks and also supports speech and text, reinforcement learning and recurrent nets. Features An application can easily switch between CPU and GPU since Caffe is written in C++ with CUDA (a parallel computing platform).
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