Machine Learning

Realtime facemask detection | 100% accurately

Facemask detection using deep learning techniques. In this blog, you will learn how to accurately detect the face mask and classify it into two classes (face mask and no face mask). For this purpose First, we trained the deep learning CNN model(Resnet) using the facemask dataset. After training the model, we used that model with …

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Generative adversarial networks

“Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don’t belong to any real person. “ Now Train The Model Results

Distance Measure in Python

Computer Vision is one powerful tool that helps in predicting various tasks.In this blog, you will learn how to measure the distance between a person and a camera. step 1 download the Haarcasde classifier to detect faces in real-time (haarcascade_frontalface_default.xml). https://github.com/kipr/opencv/tree/master/data/haarcascades Code:

Fake News detection using machine learning with flask web application

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before.Consumers are creating and sharing more information than ever before thanks to social media platforms, some of it is inaccurate …

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Uber Rides Prediction using machine learning

As ride-hailing services become more popular, being able to reliably estimate demand can help operators allocate drivers to clients more efficiently, reducing idle time, improving traffic congestion, and improving the passenger experience. In this Blog, we are making a Flask based web application that will predict the Number of Weekly Rides using machine learning model. …

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Self-Supervised Learning?

Self-Supervised Learning (Self-Supervised Learning) is a new paradigm between unsupervised and supervised learning, which aims to reduce the challenging demand for large amounts of annotated data. It provides some kind of supervision signals for feature learning by defining annotation-free pretext tasks. The term self-supervised learning (SSL) has been used (sometimes differently) in different contexts and fields, such …

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