This tutorial presents an example of application of RNN to text classification using padded and bucketed data to efficiently handle sequences of varying lengths. Some functionalities require running on a GPU with CUDA. Example based on sentiment analy...

This tutorial shows how to build and train a Conditional Generative Adversial Network (CGAN) on MNIST images. How GAN works A Generative Adversial Model simultaneously trains two models: a generator that learns to output fake samples from an unknown ...

This tutorial shows how to build and train a Conditional Generative Adversarial Network (CGAN) on MNIST images. How GAN works A Generative Adversarial Model simultaneously trains two models: a generator that learns to output fake samples from an unkn...

Machine learning is now enjoying its golden age. In the past few years, its effectiveness has been proved by solving many traditionally hard problems in computer vision and natural language processing. At the same time, different machine learning frameworks came out to justify different needs. These frameworks, fall generally into ...

GPU Accelerated XGBoost Decision tree learning and gradient boosting have until recently been the domain of multicore CPUs. Here we showcase a new plugin providing GPU acceleration for the XGBoost library. The plugin provides significant speedups over multicore CPUs for large datasets. The plugin can be found at: https://github....

Fusion and Runtime Compilation Today’s deep learning models perform tens of thousands of operations on GPU. The input and output of each GPU kernel has to be stored in the global memory, but read and write on global memory is much slower than on on-chip register. When some special ...

Introduction On March 2016, we released the first version of XGBoost4J, which is a set of packages providing Java/Scala interfaces of XGBoost and the integration with prevalent JVM-based distributed data processing platforms, like Spark/Flink. The integrations with Spark/Flink, a.k.a. XGBoost4J-Spark and XGBoost-Flink, receive the ...

TL;DR Do something fun, How about build your own TensorFlow with NNVM and Torch7 This is a new interesting era of deep learning, with emergence trend of new system, hardware and computational model. The usecase for deep learning is more heterogeneous,...

#MXNet Pascal Titan X benchmark Introduction MXNet (http://mxnet.dmlc.ml) team has received a Pascal Titan X (2016 new version) from NVIDIA, and has benchmarked this card on popular deep learning networks in MXNet, following the deepmark protocol. T...