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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

Viewing Pipeline - 2 Dimensional Viewing in Computer Graphics

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 Viewing Pipeline: Viewing Transformation in several steps: 1. Modeling Coordinates 2. World Coordinate System 3. Clipping Window 4. Normalize - Normalized Coordinates  5. Device Coordinate  6. ViewPort 7. Clipping First, we construct the scene in the world coordinates using the output primitives. Next, obtain a particular orientation for the window, we can set up a 2D viewing coordinate plane and define windows in the viewing coordinate system. The viewing coordinate reference frame is used to provide a method for setting up arbitrary orientations for a rectangular window.  Once the viewing reference frame is established. we can transform description in the world coordinate to viewing coordinates. After that define a viewport in normalized coordinates ( in the range from 0 to 1). 2D Viewing Pipeline can be achieved by the following steps:  1. Construct world coordinate scene using modeling coordinate transformation.  2. convert world coordinates to viewin...
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