Using GPUs to Scale and Speed-up Deep Learning, Certificate | Part time online | edX - online learning platform | United States
2 months
99 USD/full
99 USD/full
Tuition fee
Apply date
Start date


EdX is an online learning platform trusted by over 12 million users offering the Using GPUs to Scale and Speed-up Deep Learning Certificate in collaboration with IBMx. 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.

Visit the Visit programme website for more information


The Using GPUs to Scale and Speed-up Deep Learning  Certificate from EdX is offered in partnership with IBMx.

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.

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.

Programme Structure

Courses include:

Module 1 – Quick review of Deep Learning

  • 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

Key information


  • Part-time
    • 2 months
    • 2 hrs/week

Start dates & application deadlines

You can apply for and start this programme anytime.




  • Self-paced

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Tuition Fee

To always see correct tuition fees
  • International

    99 USD/full
    Tuition Fee
    Based on the tuition of 99 USD for the full programme during 2 months.
  • National

    99 USD/full
    Tuition Fee
    Based on the tuition of 99 USD for the full programme during 2 months.
  • Add a Verified Certificate for $99 USD
  • Limited access:free


Other interesting programmes for you

Our partners

Using GPUs to Scale and Speed-up Deep Learning
edX - online learning platform


Go to your profile page to get personalised recommendations!