A Convolutional Neural Network (CNN) is a powerful machine learning technique from the field of deep learning.
Do you want learn how CNNs work and how to build and train such networks? Join the webinar to learn more!
In this webinar, Dipendra Jha, Ph.D. student in Computer Science from Northwestern University, will provide a brief introduction to Deep Learning and TensorFlow, followed by actual implementation and demonstration of MNIST image classification using convolutional neural networks (CNNs).
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 presenter:
Dipendra Jha is is a fourth-year Ph.D. student in Computer Science from 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 (CPU and GPU) clusters, and their application to Material Science and Social Media Analytics. Before this, he completed his Master’s in Computer Science from Northwestern University. He worked in the field of Computer Networks, Distributed Systems and Cellular Networks in Aqualab under Prof. Fabian Bustamante. During this period, his research spanned from Web Page Performance Optimizations, Network Measurements and Community WiFi to Inter-domain Routing in Cellular Networks, IXPs and Content Distribution Networks (CDNs). He completed his Bachelors’ in Computer Engineering from Tribhuvan University in Nepal.