Text Prediction Using Recurrent Neural Networks with TensorFlow
April 26 @ 10:00 am - 11:00 am
Join the webinar to learn more!
In this webinar, Dipendra Jha and Reda Al-Bahrani will demonstrate how to use Sequence-to-sequence decoder from TensorFlow library build using Long Short Term Memory (LSTM) cells, on top of the text inputs embedded using the embedding lookup available in TensorFlow.
You will learn about:
- the required steps to accomplish text prediction from input processing, embeddings, to using LSTMs to make the prediction
- the concepts of Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTMs), followed by coding and demonstration for accomplishing the acutal text prediction using TensorFlow.
Who should attend?
This webinar will be of interest to Data Scientists, Software Engineers and Entrepreneurs in the areas of Connected Cars, Internet of Things/Industrial Internet, Medical Devices, Financial Technology (blockchain) and predictive apps/APIs of all sorts.
About the Presenters
Dipendra Jha is a fourth-year Ph.D. Candidate in Computer Engineering at Northwestern University. He is exploring the field of Deep Learning and Machine Learning using High Performance Computing (HPC) systems in the CUCIS lab under Prof. Alok Choudhary. His research focuses on scaling up deep learning and machine learning models using HPC system, and their application to accelerate Materials Discovery in the field of Materials Science and Engineering. Prior to this, he completed his Master’s in Computer Science from Northwestern University.
Reda Al-Bahrani is a Ph.D. Candidate in Computer Science from Northwestern University. He is exploring the field of Deep Learning and Machine Learning in the CUCIS lab under Prof. Alok Choudhary. His research focuses on knowledge discovery for health informatics from structured data and textual data. He worked in IT infrastructure support at Saudi Aramco and on XHQ an Operations Intelligence Software at Siemens Saudi Arabia before joining the CUCIS lab. Before this, he completed his Master’s in E-Commerce Technology from DePaul University.