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Using GPUs to Scale and Speed-up Deep Learning

Deep Learning,

Training acomplex deep learning model with a very large data set can 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 accelerate your convolutional neural network computations time on the Cloud. These chips are specifically 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 data might be sensitiveand you may not feel comfortable uploading it on a public cloud, preferring to analyze it on-premise. In this case, you need to use an in-house system with GPU support. One solution is to use IBM’s Power Systems with Nvidia GPU and Power AI. The Power AI platform 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 deploy deep learning networks on GPU accelerated hardware for several problems, including the classification of images and videos.

What you'll learn

  • Explain what GPU is, how it can speed up the computation, and its advantages in comparison with CPUs.
  • Implement deep learning networks on GPUs.
  • Train and deploy deep learning networks for image and video classification as well as for object recognition.

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Length 5 weeks
Effort 2 - 4 hours per week
Starts On Demand (Start anytime)
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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Rating Not enough ratings
Length 5 weeks
Effort 2 - 4 hours per week
Starts On Demand (Start anytime)
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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