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GTC ON-DEMAND

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Abstract:
We'll focus on the deep-learning neural network model deployment and inference on the IBM Cloud and how well Nvidia GPUs perform in this area compared to FPGAs that have been tuned for deep-learning primitives. We believe this topic is very relevant today because, with the emergence of new powerful NVIDIA GPUs, more and more artificial intelligence has become part of our daily lives, from Siri, Alexa, language translation, image recognition, to self-driving cars. The cognitive era has truly begun. Toward this end, IBM has formed a close partnership with Nvidia to offer GPU-enabled systems - both dedicated servers and on the cloud - to our customers and developers to run their cognitive workloads.
We'll focus on the deep-learning neural network model deployment and inference on the IBM Cloud and how well Nvidia GPUs perform in this area compared to FPGAs that have been tuned for deep-learning primitives. We believe this topic is very relevant today because, with the emergence of new powerful NVIDIA GPUs, more and more artificial intelligence has become part of our daily lives, from Siri, Alexa, language translation, image recognition, to self-driving cars. The cognitive era has truly begun. Toward this end, IBM has formed a close partnership with Nvidia to offer GPU-enabled systems - both dedicated servers and on the cloud - to our customers and developers to run their cognitive workloads.  Back
 
Topics:
Data Center & Cloud Infrastructure, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8760
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Abstract:

Deep learning is giving machines near human levels of visual recognition capabilities and disrupting many applications by replacing hand-coded software with predictive models learned directly from data. This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a real-world image classification problem. You will walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance. You'll also see the benefits of GPU acceleration in the model training process. On completion of this lab you will have the knowledge to use NVIDIA DIGITS to train a DNN on your own image classification dataset.

Deep learning is giving machines near human levels of visual recognition capabilities and disrupting many applications by replacing hand-coded software with predictive models learned directly from data. This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a real-world image classification problem. You will walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance. You'll also see the benefits of GPU acceleration in the model training process. On completion of this lab you will have the knowledge to use NVIDIA DIGITS to train a DNN on your own image classification dataset.

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Topics:
Science and Research
Type:
Instructor-Led Lab
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCL16120
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Abstract:

The new cuDNN v2 drop-in library accelerates deep learning applications using Caffe, Theano or Torch. Join NVIDIA’s Larry Brown for an update and learn how you can accelerate your deep neural net training.

The new cuDNN v2 drop-in library accelerates deep learning applications using Caffe, Theano or Torch. Join NVIDIA’s Larry Brown for an update and learn how you can accelerate your deep neural net training.

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Topics:
Artificial Intelligence and Deep Learning, Tools & Libraries, Computer Vision
Type:
Webinar
Event:
GTC Webinars
Year:
2015
Session ID:
GTCE109
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