data science

How to start a Career in Data Science

Data Science: Data science is a field that involves using scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is a interdisciplinary field that includes aspects of computer science, statistics, and subject-specific expertise. It is commonly used to analyze data from experiments, sensors, and research to …

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Data Science

Data science is a field that involves using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data scientists use a variety of techniques from statistics, machine learning, and computer science to analyze and interpret data, and to build predictive models and data-driven solutions to real-world problems. Data science …

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Generative Adversarial Network (GAN)

A Generative Adversarial Network (GAN) is a type of machine learning model that is composed of two parts: a generator and a discriminator. The generator model generates fake data, such as fake images, while the discriminator model attempts to classify the fake data as real or fake. The generator and discriminator models are trained together …

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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:

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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