Learn Data Science using Python

Another post starts with you beautiful people! Last year I had shared a post about  installing and compiling Darknet YOLOv3   in your Windows machine and also how to detect an object using  YOLOv3 with Keras . This year on April' 2020 the fourth generation of YOLO has arrived and since then I was curious to use this as soon as possible. Due to my project (built on YOLOv3 :)) work I could not find a chance to check this latest release. Today I got some relief and successfully able to install and compile YOLOv4 in my machine. In this post I am going to share a single shot way to do the same in your Windows 10 machine. If your machine does not have GPU then you can follow my  previous post  by just replacing YOLOv3 related files with YOLOv4 files. For GPU having Windows machine, follow my steps to avoid any issue while building the Darknet repository. My machine has following configurations: Windows 10 64 bit Intel Core i7 16 GB RAM NVIDIA GeForce GTX 1660 Ti Version 445.87

Another post starts with you beautiful people! Most of the businesses are adopting digital transformation to modernize customer communication and improve internal processes. By personalizing the user experience whether in a chatbot conversation, on a website or in email, you can make your user feel more valued and understood.  Google DialogFlow  and  Amazon Lex    are two pioneer vendors for building end to end personalized chatbot applications. In this post we are going to use Amazon Lex to build our chatbot and after the end of this post you will have your chatbot integrated with a web page and also your web page will be deployed on AWS cloud. This post is going to be long and very interesting so stay focus and keep reading the post till the end. Step 1. Creating your account in AWS To proceed with this post you must have an AWS account. If you don't have , just follow  this link   to create a free tier account there. While registration it may ask for your card details. No