Using GPUs to Scale and Speed-up Deep Learning
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
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.
Get a Reminder
Rating | Not enough ratings |
---|---|
Length | 5 weeks |
Effort | 5 weeks, 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 |
Get a Reminder
Similar Courses
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Learning Services $59k
Computer Vision, Deep Learning Engineer $67k
Computer Vision & Deep Learning Engineer $67k
Deep Clean Sales Specialist $76k
Deep clean specialist $76k
Deep Learning Research Scientist $86k
Deep Learning Research Engineer $88k
Research Scientist - Deep Learning $91k
Senior Learning Specialist, Learning and Development $102k
Deep Learning R&D Engineer $127k
Learning Assitant $142k
Deep Submergence Systems Program Manager $157k
Write a review
Your opinion matters. Tell us what you think.
Please login to leave a review
Rating | Not enough ratings |
---|---|
Length | 5 weeks |
Effort | 5 weeks, 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 |
Similar Courses
Sorted by relevance
Like this course?
Here's what to do next:
- Save this course for later
- Get more details from the course provider
- Enroll in this course