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AI

Introduction   This blog introduces an interesting application of conditional generative adversarial network (cGAN) for face aging. That is, you can use this cGAN to synthesize the face images of one person at different ages. For research area, this method can be used to improve the performance of “cross-age facial recognition”. For daily application, except for entertainment, it can also be used for finding missing children. This blog...

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Machine vision is the incorporation of computer vision into industrial manufacturing processes, although it does differ substantially from computer vision. In general, computer vision revolves around image processing. Machine vision, on the other hand, uses digital input and output to manipulate mechanical components. Devices that depend on machine vision are often found at work in product inspection, where they often use digital cameras or other...

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There are many applications for machine learning, whether it be processing social media traffic and trying to surface actionable insights or targeting consumers based on past purchases.  In this article aimed at those interested in artificial intelligence, we look at 10 examples of machine vision in manufacturing which include the following: Predictive Maintenance Package Inspection Reading Barcodes Product and Components Assembly Defect Reduction 3d Vision...

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machine vision system acquires images of an object, and then uses computers to process, analyze and measure various characteristics of that object. The purpose may be to enhance the image to see characteristics undetected to the human eye, or to analyze image data for measurement purposes. The decisions made from this information are often related to quality. For example, production line parts can be qualified...

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This article will entail a framework for creating and discovering new bio-inspired algorithms both for machine learning and engineering purposes as well as looking at other potential fields that might offer solutions within a computational model. It is in a way a brainstorm for a possibility. As you might be familiar, Brain-inspired Computing applies to fields from Brain-inspired computer chips produced by companies like IBM(examples: EU-backed...

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Today we will start our journey to the world of Artificial Intelligence(AI). We will learn the basic definition of Artificial Intelligence (AI), Machine Learning(ML), Deep Learning(DL), Natural Language Processing(NLP), Computer Vision and Image Processing. Later we will go deeper with the machine learning algorithms and how those algorithm works. This tutorial is for beginners, if you have an idea of AI skip this course and...

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In machine learning, there’s something called the “No Free Lunch” theorem, which means no algorithm performs best for every problem. So, you need to figure out which algorithm is best for your problem with the available data set.  In today’s blog I will focus on 10 most commonly used machine learning algorithms. As we are going to learn 10 different algorithms in this post, it will...

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What is Supervised Machine Learning? In Supervised Machine learning, the Machine is given a set of data which already knows how the output should look and have an idea about the relation between the input and out. Supervised learning problems are also categorized as “regression” and “classification” problems. In regression problems machine predict a numeric or continuous variable output where as in classification problems the predicted output is discrete.  For example, if the...

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What is machine learning?   Here’s a basic definition of machine learning: “Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions” An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences...

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Do we need to study traditional computer vision since deep learning can handle anything more efficiently?   These are good questions. Deep learning (DL) has certainly revolutionized computer vision (CV) and artificial intelligence in general. So many problems that once seemed improbable to be solved are solved to a point where machines are obtaining better results than humans. Image classification is probably the prime example of this....

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