Using GPUs to Scale and Speed-up Deep Learning from edX

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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.

What's inside

Learning objectives

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.

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.

Syllabus

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

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

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

Module 4 – Distributed Deep Learning* Distributed Deep Learning

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for learners who need a deeper understanding of video and image classification

Ideal for learners who want to comprehend the advantages of GPU in accelerating computation

Prerequisite background knowledge is required to take this course

Provides practical implementation of deep learning networks

Taught by instructors who are recognized in the field of deep learning

Empowers learners to train and deploy deep learning networks

Reviews summary

Avoid this promotional material

According to students, this course is best avoided. All reviews are negative and indicate the course is merely a promotion for IBM's PowerAI platform. Furthermore, a previously promised free trial is no longer offered.

Career center

Learners who complete Using GPUs to Scale and Speed-up Deep Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of statistics and machine learning to extract insights from data, which can be used to inform decision-making and improve business outcomes. As a Data Scientist, you would use your skills in deep learning to develop and deploy models that can be used to solve a variety of business problems.

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Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. As a Machine Learning Engineer, you would use your skills in deep learning to develop and deploy models that can be used to solve a variety of problems, such as image recognition, natural language processing, and speech recognition.

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Deep Learning Engineer

Deep Learning Engineers are responsible for developing and deploying deep learning models. As a Deep Learning Engineer, you would use your skills in deep learning to develop and deploy models that can be used to solve a variety of problems, such as image recognition, natural language processing, and speech recognition.

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Computer Vision Engineer

Computer Vision Engineers are responsible for developing and deploying computer vision models. As a Computer Vision Engineer, you would use your skills in deep learning to develop and deploy models that can be used to solve a variety of problems, such as object detection, facial recognition, and medical imaging.

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Natural Language Processing Engineer

Natural Language Processing Engineers are responsible for developing and deploying natural language processing models. As a Natural Language Processing Engineer, you would use your skills in deep learning to develop and deploy models that can be used to solve a variety of problems, such as text classification, machine translation, and question answering.

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Robotics Engineer

Robotics Engineers are responsible for designing, developing, and deploying robots. As a Robotics Engineer, you would use your skills in deep learning to develop and deploy models that can be used to control robots and make them more intelligent.

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Software Engineer

Software Engineers are responsible for designing, developing, and deploying software. As a Software Engineer, you could use your skills in deep learning to develop and deploy models that can be used to improve the performance of software applications.

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Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data. As a Data Analyst, you could use your skills in deep learning to develop and deploy models that can be used to extract insights from data.

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Business Analyst

Business Analysts are responsible for understanding the needs of businesses and developing solutions to meet those needs. As a Business Analyst, you could use your skills in deep learning to develop and deploy models that can be used to improve the performance of businesses.

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Product Manager

Product Managers are responsible for developing and managing products. As a Product Manager, you could use your skills in deep learning to develop and deploy models that can be used to improve the performance of products.

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Technical Writer

Technical Writers are responsible for creating documentation for software and other technical products. As a Technical Writer, you could use your skills in deep learning to write documentation for deep learning models and related technologies.

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Technical Support Specialist

Technical Support Specialists are responsible for providing technical support to users of software and other technical products. As a Technical Support Specialist, you could use your skills in deep learning to help users troubleshoot problems with deep learning models and related technologies.

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Sales Engineer

Sales Engineers are responsible for selling software and other technical products. As a Sales Engineer, you could use your skills in deep learning to help customers understand the benefits of deep learning and how it can be used to solve their business problems.

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Project Manager

Project Managers are responsible for planning, organizing, and executing projects. As a Project Manager, you could use your skills in deep learning to manage projects that involve the development and deployment of deep learning models.

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Academic Researcher

Academic Researchers are responsible for conducting research in a variety of fields. As an Academic Researcher, you could use your skills in deep learning to conduct research on new deep learning algorithms and architectures and to apply deep learning to new problems.

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Using GPUs to Scale and Speed-up Deep Learning

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