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DTSTART;TZID=America/New_York:20231013T113000
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SUMMARY:Student Learning Hub: Introduction to Neural Networks (NNs): How and When to Use Recurrent NNs and Convolution NNs
DESCRIPTION:Instructor: Haggai D. Davis\, III\, New York University\nHands-on exercise: Yes\nBeginner level: No prior experience required.\nSchedule: Friday Oct 13th\, 11:30am-12:30pm\nDescription: Machine learning is an incredibly broad term that encompasses a lot of different algorithms which can be applied to solve a variety of problems.  Neural networks are one category of ML algorithms which consist of layers of nodes (or neurons) which will activate to transmit information when certain thresholds are met. RNNs and CNNs are subclasses of neural networks whose differing structures allow for the analysis of different types of datasets\, usually things like time-series and pattern-recognition\, respectively.\n\n 
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-introduction-to-neural-networks-nns-how-and-when-to-use-recurrent-nns-and-convolution-nns/
CATEGORIES:Student Events
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DTSTART;TZID=America/New_York:20231020T120000
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DTSTAMP:20260503T082023
CREATED:20230927T162721Z
LAST-MODIFIED:20231003T194908Z
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SUMMARY:Student Learning Hub: Contextualising Cloud-based Geospatial Analytics Techniques
DESCRIPTION:Instructor: Vidisha Chowdhury\, University of Pennsylvania & Philippe Schicker\, Carnegie Mellon University\nHands-on exercise: Yes\nBeginner level: No prior experience required.\nSchedule: Friday October 20th\, 12-1 pm\n\nDescription: Google Earth Engine (GEE) and other cloud-based geospatial analysis platforms can be powerful tools for data scientists and researchers to analyze vast amounts of satellite imagery and geospatial data. In this one-hour introductory course\, we are going to provide you with a basic understanding of GEE\, contrast it to traditional geospatial techniques\, and highlight how GEE can be used for applications such as environmental monitoring\, land cover classification\, and more.
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-contextualising-cloud-based-geospatial-analytics-techniques/
CATEGORIES:Student Events
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DTSTART;TZID=America/New_York:20231027T120000
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DTSTAMP:20260503T082023
CREATED:20230927T162816Z
LAST-MODIFIED:20231003T191918Z
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SUMMARY:Student Learning Hub: Solving Large-Scale Optimization Problems: A Practical Guide to High-Performance Computing (HPC) and Using C++ APIs for CPLEX
DESCRIPTION:Instructor: Yuhao Liu\, NYU Shanghai\nHands-on exercise: Yes\nBeginner level: No prior experience required.\nSchedule: Friday Oct 27th\, 12-1pm\n\nDescription: This course is designed to equip the audience with the basic knowledge and skills necessary to navigate the world of high performance computing (HPC) and leverage IBM’s CPLEX optimization studio for tackling complex optimization challenges. In this course\, you will learn 1) how to run computation tasks on HPC clusters; 2) how to solve optimization problems using the C++ API of CPLEX; and 3) how to deploy and run CPLEX programs on HPC clusters for large-scale optimization models and/or parallel optimization algorithms.
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-solving-large-scale-optimization-problems-a-practical-guide-to-high-performance-computing-hpc-and-using-c-apis-for-cplex/
CATEGORIES:Student Events
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