Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
", "fullDescription": "Please Note: Learners who successfully complete this IBM course can earn a skill badge \u2014 a detailed, verifiable and digital credential that profiles the knowledge and skills you\u2019ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
\nTraining 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.
\nYou can use accelerated hardware such as Google\u2019s 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.
\nBut 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\u2019s 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.
\nIn 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.
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\nIntro to Deep Learning
\n Deep Learning Pipeline
Module 2 \u2013 Hardware Accelerated Deep Learning
\nHow to accelerate a deep learning model?
\n Running TensorFlow operations on CPUs vs. GPUs
\nConvolutional Neural Networks on GPUs
\n Recurrent Neural Networks on GPUs
Module 3 \u2013 Deep Learning in the Cloud
\nDeep Learning in the Cloud
\n How does one use a GPU
Module 4 \u2013 Distributed Deep Learning
\n* Distributed Deep Learning
Module 5 \u2013 PowerAI vision
\nComputer vision
\n Image Classification
\n* Object recognition in Videos.