IACAT Redesign
First 100 matches found
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This tutorial will teach how to gain access to the system, how to interact with the system through Open OnDemand interface, including Jupiter notebook, and through command line interface.
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This tutorial will cover basic machine learning techniques, such as Linear Regression, Decision Tree, Support Vector Machine, Naive Bayes, K- Nearest Neighbors, K-Means, and Random Forest, using TensorFlow on HAL system.
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This tutorial will introduce how machine learning can be accomplished with neural networks and will go over various examples from simple dense networks to convolutional network architectures using TensorFlow on HAL system.
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This tutorial will introduce sequence models, such as RNN and LSTM, and how these models can be implemented with TensorFlow on HAL system.
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This training session will be dedicated to discussing problems to be given in the first hackathon to be organized on March 7-8.
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This tutorial will teach how to use multiple GPUs for accelerating deep learning on HAL.
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This tutorial will introduce concepts from reinforcement leaning and how this can be implemented on HAL system.