Introduction to Multimodal Deep Learning

Multimodal Deep Learning—Challenges and Potential

Modality refers to how a particular subject is experienced or represented. Our experience of the world is multimodal—we see, feel, hear, smell and taste things. Multimodal deep learning tries to link and extract information from data of different modalities. Just as the human brain processes signals from all senses at once, a multimodal deep learning model extracts relevant information from different types of data in one go.

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Easing Overload in Video Tracking Applications

Easing Server Overload in Video Tracking Applications

Video tracking applications are becoming commonplace with a growing number of use cases such as perimeter surveillance, asset tracking, medical imaging, and traffic violation monitoring. One of the problems that a developer must tackle while building a video tracking application is server overload.

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Comparative Analysis of ML Models for Fraud Detection

Comparative Analysis of ML Models for Fraud Detection

A large variety of fraud patterns combined with insufficient data on fraud makes insurance fraud detection a very challenging problem. Many algorithms are available today to classify fraudulent and genuine claims. To understand the various classification algorithms applied in fraud detection, I did a comparison using vehicle insurance claims data. 

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Conversational AI chat-bot — Architecture overview

Conversational AI Chatbot: Architecture Overview

Artificial Intelligence (AI) powers several business functions across industries today, its efficacy having been proven by many intelligent applications. Of the lot, chatbots are perhaps the most well-known. From healthcare to hospitality, retail to real estate, insurance to aviation, chatbots have become a ubiquitous and useful feature. But how are these chatbots created? Let’s take a look at the architecture of a conversational AI chatbot.

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