“A deep loss to the global community of Data Science, Big Data, and Emerging Technologies”
Students from NJBDA member institutions are eligible for Rutgers MBS - NJBDA joint externship exchange program.
Knowledge Graphs in Natural Language Processing: Overcome Domain Adaptation Challenges in Language Models. Event presented by Vivek Kumar, Marie Sklodowska-Curie Scholar, University of Cagliari, Italy.
Recording now available: Intelligent Informatics Event with Experts from Microsoft’s AI for Good lab: Open Data for Society: Accelerating Solutions for Society’s Most Pressing Challenges, Presentation, Work-along and Q&A.
It is our great pleasure to announce the call for manuscripts for the Journal of Big Data Theory and Practice (JBDTP) special issue - Enabling Technologies in Intelligent Healthcare: From the Internet of Things (IoT), To Artificial Intelligence, Big Data, and Blockchain.
New Jersey Big Data Alliance publishes first issue of flagship Journal of Big Data: Theory and Practice
The New Jersey Big Data Alliance (NJBDA) has taken an important step in catalyzing research collaborations in big data, artificial intelligence, and machine learning for both researchers and practitioners with the inaugural release of its new journal.
It is our great pleasure to announce the call for papers for the Journal of Big Data: Theory and Practice (JBDTP). JBDTP is an open access peer-reviewed journal which is devoted to the publication of high-quality papers on the theoretical and practical aspects of big data, AI, applications and machine learning. Authors are invited to submit novel, high quality work that has neither appeared in nor is under consideration for publication elsewhere.
Collaborating partners TechUnited, Edge, the New Jersey Economic Development Authority (NJEDA), and the New Jersey Big Data Alliance (NJBDA), are pleased to announce the availability of the State of The State of Innovation in New Jersey Report.
This workshop focuses on learning the basics of Deep Learning with Python and Keras including data preparation, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
In this workshop, after a brief overview on machine learning, we will focus on doing the hands-on training in applying ML models on various data types including image, text, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods.