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SUMMARY:2022 NJBDA Symposium call for abstracts
DESCRIPTION:The NJBDA Annual Symposium brings together academia\, government and industry from across the state and beyond\, to share information on the latest innovations\, research and future directions in Big Data. The concept for this year is: Education and Training of a Big Data Workforce: Building a Pipeline for a Data-Driven Economy. We invite faculty/researchers to submit abstracts of their research for presentation at the symposium. \n\n\n\nAbstracts should be a maximum of 650 words and submitted by March 25\, 2022. Indicate your name\, email\, department\, university affiliation\, title of presentation and track\, at the top of the abstract. This information does not count towards the 650-word limit. Notification of acceptance by April 15\, 2022.  \n\n\n\nSubmit Abstract to: https://easychair.org/conferences/?conf=9thannualnjbdasympos \n\n\n\nMore information: Call for Abstracts
URL:https://njbda.org/event/2022-njbda-symposium-abstract-submittal-deadline-march-25/
LOCATION:New Jersey
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SUMMARY:Data Science Workshop: Deep Learning with Python
DESCRIPTION:Watch the recording by clicking on the image above\n\n\n\nDeep Learning (DL) outperforms Machine Learning (ML) in many of the applications related to the Computer Vision (CV) and Natural Language Processing (NLP). One of the biggest advantages of DL over ML is that they can automatically extract the important features from the data. With sufficient data and compute power\, DL methods\, in particular the supervised learning methods\, can achieve the prediction accuracies that were not seen before with any other statistical methods in applications related to CV and NLP. \n\n\n\nIn this workshop\, we will go through the basics of artificial neural networks (ANN)\, Convolutional Neural Networks (CNN)\, and Recurrent Neural Networks (RNN)\, and do hands-on training with these DL models to build predictive analytics for image and text data. \n\n\n\nObjective of the workshop \n\n\n\nUnderstand the basics of Artificial Neural Networks (ANN)Prepare image and text data suitable for the neural networksLearn how to apply various DL models such as ANN\, CNN\, and RNNImprove the accuracy of the model with Hyperparameter Optimization\n\n\n\nWhat is needed? Laptop/Desktop with Internet connection \n\n\n\nDuration: 3 hours \n\n\n\nLevel: Intermediate \n\n\n\nProgramming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: Will be provided in the workshop \n\n\n\nThis workshop is hosted by the Office of Advanced Research Computing (OARC)\, Rutgers University\, organized by Bala Desinghu in collaboration with the Eastern Regional Network (ERN) and the New Jersey Big Data Alliance (NJBDA). \n\n\n\nParticipants are encouraged to attend with campus partners representing a variety of stakeholders for campus research and research computing (e.g.\, researchers\, research computing professionals\, students\, staff\, faculty\, and practitioners\, etc.). \n\n\n\nFor additional information\, feel free to contact bala.desinghu@rutgers.edu\, forough.ghahramani@njedge.net\, or gavirapp@kean.edu.
URL:https://njbda.org/event/data-science-workshop-deep-learning-with-python-and-keras/
LOCATION:New Jersey
CATEGORIES:workshops
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