About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Theano 1.0.2 (23rd of May, 2018)

This is a maintenance release of Theano, version 1.0.2, with no new features, but some important bug fixes.

We recommend that everybody update to this version.

Highlights (since 1.0.1):

• Theano should work under PyPy now (this is experimental).
• Update for cuDNN 7.1 RNN API changes.
• Fix for a crash related to mixed dtypes with cuDNN convolutions.
• MAGMA should work in more cases without manual config.
• Handle reductions with non-default accumulator dtype better on the GPU.
• Improvements to the test suite so that it fails less often due to random chance.

A total of 6 people contributed to this release since 1.0.1:

• Frederic Bastien
• Steven Bocco
• Jon Haygood
• Arnaud Bergeron
• Jordan Melendez
• Desiree Vogt-Lee
• Garming Sam
• Pascal Lamblin
• Vincent Dumoulin
• Glexin
• Simon Lefrancois

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

• New: A Makefile for mingw to build on Windows.
• Changed: PR #94 added a much more efficient sparse kernel.
• Changed: boilerplate code for Julia greatly improved.
• Changed: Code cleanup, pre-processor macros simplified.
• Changed: Adapted to Seaborn API changes in plotting heatmaps.

About: A Deep Learning API and server

Initial Announcement on mloss.org.

About: Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. Support element-wise expression expand in high performance. Code once, run smoothly on both GPU and CPU

Initial Announcement on mloss.org.

About: CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow(https://github.com/tqchen/mshadow). It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance.

Initial Announcement on mloss.org.