Ever read a piece of news which just seems bogus? We all encounter such news articles, and instinctively recognise that something doesn't feel right. Because of so many posts out there, it is nearly impossible to separate the right from the wrong. Here, we are not only talking about spurious claims and the factual points, but rather, the things which look wrong intricately in the language itself. Did you ever wonder how to develop a fake news detection project? But there is no easy way out to find which news is fake and which is not, especially these days, with the speed of spread of news on social media.

Implementing real time object detection using python PyTorch OpenCV. Using yolo to build real time object detection system in python. The course will teach you how to make your own classifier from only one positive image. The project is about the real time streaming, and detect objects in video games as well.

Posenet is a real-time pose detection technique with which you can detect human poses in Image or Video. let's make a real-time project.

If you've been studying data science for a while, you might know that in order to learn data science you need to learn math, statistics, and programming. This is a good start for anyone interested in data science, but do you know how to get even more exposure to data science? A project will help you put into practice all the knowledge you've acquired from math, statistics, and programming. So far you might've seen each of them individually, but after you finish a project, the concepts you've learned in each field will make more sense. In this article, I listed some end-to-end data science projects you can do with Python.

Real-time object detection project Click here to download the source code to this post ... To build our deep learning-based real-time object detector with OpenCV we'll need to (1) ... The course will teach you how to make your own classifier from only one positive image. The project is about the real time streaming, and detect objects in video games as well.

Working with object detection in deep learning can sometimes be difficult but it is extremely fun. And it feels even more rewarding when we are able to solve the detection problem on our custom dataset. But when working with a custom dataset, how do we know that our object detection algorithm has reached its optimal performance? How do we know that going through a few more epochs won't lead to overfitting of the deep learning algorithm? In fact, these questions are really important when working to competitive datasets like PASCAL VOC or MS COCO dataset as well. These competition datasets have pretty stringent object detection evaluation metrics.