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Using GPUs to Scale and Speed-up Deep Learning

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.

Using GPUs to Scale and Speed-up Deep Learning

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After a course session ends, it will be archived.
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Estimated 5 weeks
2–4 hours per week
Self-paced
Progress at your own speed
Free
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About this course

Skip About this course

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.

At a glance

  • Language: English
  • Associated programs:

What you'll learn

Skip 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.

Module 1 – Quick review of Deep Learning
Intro to Deep Learning
Deep Learning Pipeline

Module 2 – Hardware Accelerated Deep Learning
How to accelerate a deep learning model?
Running TensorFlow operations on CPUs vs. GPUs
Convolutional Neural Networks on GPUs
Recurrent Neural Networks on GPUs

Module 3 – Deep Learning in the Cloud
Deep Learning in the Cloud
How does one use a GPU

Module 4 – Distributed Deep Learning
* Distributed Deep Learning

Module 5 – PowerAI vision
Computer vision
Image Classification
* Object recognition in Videos.

About the instructors

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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