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Tag: computer vision

Color Picker Application Using Computer Vision

This article was published as a part of the Data Science Blogathon. Overview In this article, we will be making a very interesting application i.e. Color picker which have many use cases but the main use case of this application is that it can be widely used by UI/UX designer who has to pick the […]

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Blood Cell Detection in Image Using Naive Approach

This article was published as a part of the Data Science Blogathon. The basics of object detection problems are how the data would look like. Now, this article will discuss the different deep learning architectures that we can use to solve object detection problems. Let us first discuss the problem statement that we’ll be working […]

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Intelligent asset management and the race to Zero D

In an earlier post, IBM industry expert Scott Campbell talked about how manufacturers are pursuing resiliency and Zero D to stop defects and improve products and service quality. In part two of our discussion, he discusses how mitigating rework can save millions and offers some insights on the value of creating citizen data scientists. Can […]

The post Intelligent asset management and the race to Zero D appeared first on IBM Business Operations Blog.

Foxconn to Invest $100m in XRSPACE’s Metaverse Vision

The first installment will be $15 million.

XRSPACE teams up with Foxconn to conquer the Metaverse

XRSPACE teams up with Foxconn to conquer the Metaverse - Main 1

XRSPACE has landed $15 million of investment from electronics manufacturer Foxconn, in a partnership that could be worth $100 million

The post XRSPACE teams up with Foxconn to conquer the Metaverse appeared first on VRWorldTech Magazine.

Approaching Regression with Neural Networks Using Tensorflow

This article was published as a part of the Data Science Blogathon. Introduction Every supervised machine learning technique basically solves either classification or regression problems. Classification is a kind of technique where we classify the outcome into distinct categories whose range is usually finite. Whereas a regression technique involves predicting a real number whose range is […]

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Comparing R and Tableau for Data Visualisation

This article was published as a part of the Data Science Blogathon. There has been a debate in the industry between R and Tableau. Which is better is the question. Let us see this in today’s article.   Source – Author What is Data Visualization? Data visualization is an interdisciplinary field that uses visual elements […]

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Let’s Learn Face Detection Using Computer Vision

This article was published as a part of the Data Science Blogathon. Overview In this article, we will be making a face detection application it will detect a single face in the image and detect multiple faces at the same time (if found), hence, the entire article will focus on Face detection using computer vision. […]

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The Complete Collection of Data Science Cheat Sheets – Part 2

A collection of cheat sheets that will help you prepare for a technical interview on Data Structures & Algorithms, Machine learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks.

How You Can Use Machine Learning to Automatically Label Data

AI and machine learning can provide us with these tools. This guide will explore how we can use machine learning to label data.

A Basic Introduction to OpenCV in Deep Learning

This article was published as a part of the Data Science Blogathon. OpenCV is a massive open-source library for various fields like computer vision, machine learning, image processing and plays a critical function in real-time operations, which are fundamental in today’s systems. It is deployed for the detection of items, faces, Diseases, lesions, Number plates, […]

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A Quick Guide to Bivariate Analysis in Python

This article was published as a part of the Data Science Blogathon. Introduction In all kinds of data science projects across domains, EDA (exploratory data analytics) is the first go-to analysis, without which the analysis is incomplete or almost impossible to do. One of the key objectives in many multi-variate analyses is to understand relationships between […]

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