Using GPUs to Scale and Speed-up Deep Learning

Training acomplex deep learning model with a very large datasetcan take hours, days and occasionally weeks to train. So, what is the solution? Accelerated hardware. You can use accelerated hardware such as Google’s Tensor Processing Unit(TPU) or Nvidia GPU to accelerateyourconvolutional neural network computations timeon the Cloud. These chips arespecifically designed to support the training of neural networks, as well as the use of trained networks(inference).Accelerated hardware has recently been proven to significantly reduce training time. But the problem is that your datamight be sensitiveand you may not feel comfortable uploading iton apublic cloud, preferring to analyze it on-premise.In this case, youneed to use an in-house system withGPU support. One solution isto useIBM’s Power SystemswithNvidia GPU andPowerAI. ThePowerAIplatform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano. In this course, you'll understand what GPU-based accelerated hardware is and how it can benefit your deep learning scaling needs. You'll also deploydeep learning networks on GPU accelerated hardware for several problems, including the classification ofimages and videos.