Keras: Deep Learning library for Theano and TensorFlow You have just found Keras. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least Read more about Keras – deep learning library for Tensorflow and Theano[…]
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Source: Caffe | Deep Learning Framework
Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework that puts Python first. We are in an early-release Beta. Expect some adventures. Source: PyTorch
Machine learning is cropping up more and more in research papers – does it work? Source: From drugs to galaxy hunting, AI is elbowing its way into boffins’ labs • The Register
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss Read more about Image-to-Image Translation with Conditional Adversarial Networks[…]
Astronomers have found a system of seven Earth-sized planets just 40 light-years away. They were detected as they passed in front of their parent star, the dwarf star TRAPPIST-1. Three of them lie in the habitable zone and could harbour water, increasing the possibility that the system could play host to life. It has both Read more about Temperate earth-sized worlds found in extraordinarily rich planetary system[…]
ETSI is pleased to announce the creation of the Industry Specification Group ‘Experiential Network Intelligence’ (ISG ENI). The purpose of the group is to define a Cognitive Network Management architecture that is based on the “observe-orient-decide-act” control model. It uses AI (Artificial Intelligence) techniques and context-aware policies to adjust offered services based on changes in Read more about New ETSI group on improving operator experience using Artificial Intelligence[…]