Note: The program that this course is a part of is changing and this course will permanently close on April 30, 2020. The remaining courses in the IBM Deep Learning Professional Certificate program will remain available.
Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in 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.
Youcan 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.
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