ELEKTRONN: A highly configurable toolkit for training 3d/2d CNNs and general Neural Networks Project Website: http://elektronn.org/ Github Link: https://github.com/ELEKTRONN/ELEKTRONN Description ELEKTRONN is a deep learning toolkit that makes powerful neural networks accessible to scientists outside of the machine learning community. … Continue reading

# Tag Archives: CPU

ConvNet: Convolutional Neural Networks for Matlab Project Website: None Github Link: https://github.com/sdemyanov/ConvNet Description Convolutional Neural Networks for Matlab, including Invariang Backpropagation algorithm (IBP). Has versions for GPU and CPU, written on CUDA, C++ and Matlab. All versions work identically. The … Continue reading

neuralnetworks: Deep Neural Networks with GPU support Project Website: None Github Link: https://github.com/ivan-vasilev/neuralnetworks Description This is a Java implementation of some of the algorithms for training deep neural networks. GPU support is provided via the OpenCL and Aparapi. The architecture … Continue reading

deepmat Project Website: None Github Link: https://github.com/kyunghyuncho/deepmat Description = Generative Stochastic Network = A simple implementation of GSN according to (Bengio et al., 2013) = Convolutional Neural Network = A naive implementation (purely using Matlab) Pooling: max (Jonathan Masci’s code) … Continue reading

Deeplearning4J: Neural Net Platform Project Website: http://deeplearning4j.org/ Github Link: https://github.com/deeplearning4j/deeplearning4j Description Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on … Continue reading

Pylearn2: A machine learning research library Project Website: http://deeplearning.net/software/pylearn2/ Github Link: https://github.com/lisa-lab/pylearn2 Description Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) … Continue reading