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Using GPUs to Scale and Speed-up Deep Learning

Using GPUs to Scale and Speed-up Deep Learning

FREE (Audit)
July 4
15 hours
English
IBM
Course by
edXCourses from edX
Certificate awarded
For Rs. 7018
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is not from a very prestigious university.

Price

This course is costly - Rs. 7018/-.

Edvicer's Rewards

You can get a cashback of ₹ 250 on buying this course.

Why should you choose this course?

Description

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.

Syllabus

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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