Using GPUs to Scale and Speed-up Deep Learning

Provided by IBM
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Training complex deep learning models with large datasets takes a long time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

Part of 1 program:
Course Format:Instructor-Led
Start Date:Mar 20, 2019

What you will 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.

Overview

Training a complex deep learning model with a very large dataset 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 sensitive and 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 PowerAI. The PowerAI 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.

Before you start

  • No prerequisites
  • Instructor-Led: course contains assignments and exams that have specific due dates, and you complete the course within a defined time period.
  • Course ends: Mar 20, 2019

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