The term “LLM” has been generating a lot of buzz on social media, with mentions often carrying an air of mystery or awe. What are LLMs exactly, and in which areas are they making a significant impact? This blog post is an attempt to shed light on these questions.

In this blog post, we will explore the growing significance of chatbots for e-commerce, their limitations, and how GPT-3 can enhance their capabilities. We’ll discuss how GPT-3 can improve intent detection, entity extraction, and conversational flow building, and thus make chatbots more robust and efficient.

In Part 1, we discussed how we extract text from invoices using PaddleOCR. Here we will outline the steps for retrieving relevant information from the extracted text using deep learning.

Manual data entry presents a serious bottleneck for organizations that have a vast number of documents to process. With the coming of age of intelligent data extraction and document processing technologies (computer vision, natural language processing, machine learning, robotic process automation, etc.), they can now avail solutions that halve the labor and double the speed.

There are two major steps involved in intelligent invoice processing: text extraction from invoices and information extraction from the extracted text. We will cover these steps in a two-part series based on the intelligent invoice processing solution we built for a client.

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.