Using GPUs to Scale and Speed-up Deep Learning
Master the techniques of utilizing graphics processing units to accelerate neural network training and optimize deep learning model performance metrics.
Master the techniques of utilizing graphics processing units to accelerate neural network training and optimize deep learning model performance metrics.
This course teaches how to use GPU-accelerated hardware to overcome scalability challenges in deep learning. Learn about GPU technology, its advantages over CPUs, and how to implement deep learning networks on GPUs. Explore cloud-based and on-premise GPU solutions, including IBM's Power Systems with NVIDIA GPUs. Gain hands-on experience in deploying deep learning networks for image and video classification, as well as object recognition, using GPU-accelerated hardware.
Instructors:
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What you'll learn
Understand the benefits of GPU acceleration for deep learning
Implement deep learning networks on GPU-accelerated hardware
Compare performance of TensorFlow operations on CPUs vs. GPUs
Deploy Convolutional and Recurrent Neural Networks on GPUs
Explore cloud-based and on-premise GPU solutions for deep learning
Apply distributed deep learning techniques for improved scalability
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 5 modules in this course
This course focuses on using GPU-accelerated hardware to enhance the speed and scalability of deep learning models. The curriculum begins with a quick review of deep learning concepts before diving into the specifics of hardware acceleration. Students will learn about the advantages of GPUs over CPUs for deep learning computations and how to implement deep learning networks on GPUs. The course covers both cloud-based solutions, such as Google's Tensor Processing Unit (TPU), and on-premise options like IBM's Power Systems with NVIDIA GPUs. Practical modules include running TensorFlow operations on GPUs, implementing Convolutional and Recurrent Neural Networks on GPUs, and exploring distributed deep learning. The course culminates with a focus on computer vision applications, including image classification and object recognition in videos using GPU-accelerated systems.
Quick Review of Deep Learning
Module 1
Hardware Accelerated Deep Learning
Module 2
Deep Learning in the Cloud
Module 3
Distributed Deep Learning
Module 4
PowerAI Vision
Module 5
Fee Structure
Instructor
38 Courses
Pioneering Data Scientist Leading Enterprise Analytics Innovation
Saeed Aghabozorgi, PhD, serves as a Senior Data Scientist at IBM, where he specializes in developing enterprise-level applications that transform complex data into actionable business knowledge. His expertise spans data mining, machine learning, and statistical modeling, with particular emphasis on large-scale datasets. As an accomplished educator, his courses have reached over 100,000 learners worldwide, maintaining an impressive 4.7 instructor rating. His most notable contribution includes the Machine Learning with Python course, which has enrolled more than 482,000 students and covers comprehensive topics from supervised learning to advanced clustering techniques. Through his work at IBM, he continues to advance the field of data science by developing cutting-edge analytical methods and sharing his expertise through educational initiatives that bridge the gap between theoretical knowledge and practical application.
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