About: APRILANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising autoencoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others. Changes:
C/C++

About: This algorithm is described in Deep Semantic Ranking Based Hashing for MultiLabel Image Retrieval. See https://github.com/zhaofang0627/cudaconvnetforhashing Changes:Initial Announcement on mloss.org.

About: Hype is a proofofconcept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization. Changes:Initial Announcement on mloss.org.

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more Changes:This version comes with Distributed and Mobile Examples

About: A Deep Learning API and server Changes:Initial Announcement on mloss.org.

About: A Theano framework for building and training neural networks Changes:Initial Announcement on mloss.org.

About: CN24 is a complete semantic segmentation framework using fully convolutional networks. Changes:Initial Announcement on mloss.org.

About: The autoencoder based data clustering toolkit provides a quick start of clustering based on deep autoencoder nets. This toolkit can cluster data in feature space with a deep nonlinear nets. Changes:Initial Announcement on mloss.org.

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building productiongrade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details. Changes:Adding a large number of new distributions, such as AndersonDaring, ShapiroWilk, Inverse ChiSquare, Lévy, Folded Normal, Shifted LogLogistic, Kumaraswamy, Trapezoidal, Uquadratic and BetaPrime distributions, BirnbaumSaunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions. Other additions include new statistical hypothesis tests such as AndersonDaring and ShapiroWilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others. For a complete list of changes, please see the full release notes at the release details page at: https://github.com/accordnet/framework/releases

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of stateoftheart deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on nonGPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode). Changes:LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999

About: Crino: a neuralnetwork library based on Theano Changes:1.0.0 (7 july 2014) :  Initial release of crino  Implements a torchlike library to build artificial neural networks (ANN)  Provides standard implementations for : * autoencoders * multilayer perceptrons (MLP) * deep neural networks (DNN) * input output deep architecture (IODA)  Provides a batchgradient backpropagation algorithm, with adaptative learning rate

About: GPUaccelerated java deep neural networks Changes:Initial Announcement on mloss.org.

About: PLearn is a large C++ machinelearning library with a set of Python tools and Python bindings. It is mostly a research platform for developing novel algorithms, and is being used extensively at [...] Changes:Initial Announcement on mloss.org.
