Node Classification Using GNN: A Case Study

Node Classification Using GNN: A Case Study

Graph Neural Networks (GNN) have proven their capability in traffic forecasting, recommendation systems, drug discovery, etc., with their ability to learn from graph representations. What I’m going to do here is take you through the working of a simple Graph Neural Network and show you how we can build a GNN in PyTorch to solve the famous Zachary Karate Club node classification problem.

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PyTorch Lightning: A Better Way to Write PyTorch Code

PyTorch Lightning: A Better Way to Write PyTorch Code

Scaling machine learning pipelines using PyTorch can be a pain. 

You typically start a PyTorch-based machine learning project by defining the model architecture. Then you run it on a CPU machine and progressively create a training pipeline. Once the pipeline is done, you run the same code on a GPU or TPU machine for faster gradient computations. You update the PyTorch code to load all the tensors to the GPU/TPU memory with a ‘.to(device)’ function call. Now comes the difficult part: what if you want to use distributed training for the same pipeline? You have to overhaul the code and test it to make sure nothing is broken.

Why sweat the small stuff? Let’s use PyTorch Lightning instead.

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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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Automated Data Extraction from Medical Records

Automated Data Extraction from Lab Reports

Regardless of the many technological leaps made over the past decade, firms in the healthcare, insurance, and finance sectors still deal with a staggering amount of paperwork. Because of the lack of unified data platforms, it is still a common practice to use paper documents when there is a need for ad hoc data transfer between organizations. This is especially true in the case of insurance companies in India, which require you to attach medical records from hospitals with the claim request forms.

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Automating Insurance Claims Adjudication Process

Automating Insurance Claim Adjudication

Claim adjudication, the process of determining the financial liability of a claim by the insurance company, is quite complex and time-consuming. Adjudication can be quick if the received claim is clear to the dot, in the sense that all the information is accurate and the claim is within the limits of the policy. But, as with all things in life, this is never the case. 

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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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NLP Libraries for Malayalam Sentence Tokenization: An Exploratory Study

NLP Libraries for Malayalam Sentence Tokenization: An Exploratory Study

Imagine that you are working on machine translation or a similar Natural Language Processing (NLP) problem. Can you process the corpus as a whole? No. You will have to break it into sentences first and then into words. This process of splitting input corpus into smaller subunits is known as tokenization. The resulting units are tokens. For instance, when paragraphs are split into sentences, each sentence is a token. This is a fairly straightforward process in English but not so in Malayalam (and some other Indic languages). 

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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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