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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
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What is the difference between local descriptors and global descriptors?

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Descriptors  rely on Gradient-based or intensity variation approaches to detect Local Features (e.g., Edges, Blobs, and Regions). BLOB = Binary Large Object (i.e., the region of the image). Descriptors such as HOG, SIFT, SURF (rely on local gradient computation). Binary Descriptors such as BRISK, ORB or FREAK (rely on local intensity differences) Global Descriptor –  Describe the whole image, Local Descriptor  – Describe the patch within the image. (The best example of the global descriptor is SIFT). LDP = Local Directional Pattern. Local Directional Pattern is a  descriptor  i.e., used for Face Recognition. How to Calculate Local Directional Pattern (LDP) Code?|Kirsch Compass Mask| https://youtu.be/dY0KarfWTV4
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