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As a charitable service-based nonprofit organization (NPO) coordinating individuals, businesses, academia and governments with interests in High Technology, Big Data and Cybersecurity, we bridge the global digital divide by providing supercomputing access, applied research, training, tools and other digital incentives “to empower the underserved and disadvantaged.”

FREE WORKSHOP | Summer 2018 Data Science Bootcamp | University of Houston

  • 14 Aug 2018
  • 16 Aug 2018
  • University of Houston | MREB building (4718 Calhoun Road, Houston, TX 77204-3058), room 200

Summer 2018 Data Science Bootcamp

LIMITED SEATING

August 14-16

University of Houston

MREB building (4718 Calhoun Road, Houston, TX 77204-3058), room 200

Lunch will not be provided

 

The University of Houston's Center for Advanced Computing & Data Science, and the Data Science Institute are very happy to brig to you a Bootcamp covering Machine Learning, Python, NVIDIA tutorials, Tensorflow and R.

 

Tuesday 14, FREE

    morning session, 9:30AM-12:00PM, Python

    afternoon session, 1:00PM-4:45PM, Machine Learning

Wednesday 15, (some tutorials may enquire fees) 

  morning session, 9:30AM-12:00PM, NVIDIA Tutorials I

   afternoon session, 1:00PM-4:45PM, NVIDIA Tutorials II



Thursday 16, FREE

morning session, 9:30AM-12:00PM, Tensorflow

afternoon session, 1:00PM-4:45PM, R

 

Register TODAY 


Contact, Dr. Martin Huarte, mhuartee@central.uh.edu.


Image Classification with DIGITS

Prerequisites: None

Description: Learn how to train a deep neural network to recognize handwritten digits by loading image data to a training environment, choosing and training a network, and testing with new data and iterating to improve performance.


Object Detection with DIGITS

Prerequisites: None

Description: Learn how to detect objects using computer vision and deep learning by identifying a purpose-built network and using end-to-end labeled data.


Modeling Time Series Data with Recurrent Neural Networks in Keras

Prerequisites: Basic experience with deep learning

Description: Explore how to classify and forecast time-series data using RNNs, such as modeling a patient’s health over time.



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