Building AI-Based Emotional Detectors in Images and Text – A Hands-On Approach
Building AI-Based Emotional Detectors in Images and Text – A Hands-On Approach

Abstract: 

Deep Learning has become ubiquitous in everyday software applications and services. A solid understanding of DL foundational principles is necessary for researchers and modern-day engineers alike to successfully adapt the state of the art research in DL to business applications.

In this workshop, we will cover the basics of Deep Learning, what deep learning can and cannot do. We will learn the applications of Deep Learning where it has achieved state of the art results viz., to Images and Text.
The session will be a hands-on lab where attendees will use Apache MXNet to build an emotional detector in Images, we will cover basics of Convolutional Neural Networks applied to Computer Vision problems as we build the model.

The attendees will also build a model that detects emotions(sentiments) in text data, we will cover the basics of Recurrent Neural Networks that is widely used to solve Natural Language Processing problems.

The attendees will learn how to leverage the state of the art research to their application, best practices and tips, and tricks used by practitioners.

Bio: 

Naveen is a Senior Software Engineer and a member of Amazon AI at AWS and works on Apache MXNet. He began his career building large scale distributed systems and has spent the last 10+ years designing and developing it. He has delivered various Tech Talks at AMLC, Spark Summit, ApacheCon and loves to share knowledge. His current focus is to make Deep Learning easily accessible to Software Developers without the need for a steep learning curve. In his spare time, he loves to read books, spend time with his family and watch his little girl grow.

Open Data Science

 

 

 

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