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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning

How Digital Image is formed? ~xRay Pixy


Digital Image is Made up of Pixels. Pixel is the Smallest Element of an Image. A digital image is composed of a finite number of elements, each element has a particular location and value.
The digital image is a 2-dimensional function. It is represented by f(x,y). Where x,y is the plane coordinates, and f is the amplitude that is the brightness of the image. The sensor is used to sense the object structure and a continuous voltage signal is generated.
In order to create a digital image, we need to convert data into digital form. For this Sampling and Quantization is done.
Sampling is digitizing the coordinate value.
Quantization is analog to digital conversion of a sampled image to a finite number of gray level.
The process of digital image formation is shown in this video.
https://youtu.be/pNl-yhlp5bo

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