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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.

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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Rating Not enough ratings
Length 5 weeks
Effort 2 - 4 hours per week
Starts Jul 1 (last week)
Cost $99
From IBM via edX
Instructor SAEED AGHABOZORGI
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics Engineering

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Instructor, Accelerated Composition $45k

Accelerated Degree Program Coordinator $46k

IT Hardware Technician $52k

Accelerated Development Program- Cost Accountant $57k

Instructor, Accelerated Program $70k

Hardware PLM $85k

Accelerated Development Program (ADP) $89k

Hardware Tester $89k

Operations Leader - Accelerated Solutions Center $97k

Hardware Researcher $112k

Hardware Engineering $114k

Hardware Engineer 6 $134k

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edX

&

IBM

Rating Not enough ratings
Length 5 weeks
Effort 2 - 4 hours per week
Starts Jul 1 (last week)
Cost $99
From IBM via edX
Instructor SAEED AGHABOZORGI
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics Engineering

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