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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

OPTIMIZATION ENGINEERING | Metaheuristic Algorithms | : Basic Fundamentals

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OPTIMIZATION ENGINEERING Optimization: In Optimization we either minimize or maximize objective functions / cost function. No Free Lunch Theorem for Optimization No Free Lunch Theorem for Optimization According to No Free Lunch Theorem "There is no universal better algorithm exist that can solve all types of optimization problems". Today, Metaheuristic Optimization Algorithms are used in different areas to solve complex real work optimization problems. For example in Industrial Areas, Operation Research, Medical Field, Engineering design and other as you can see below:  History of Metaheuristic Optimization Algorithms: Genetic Algorithms (G.A.) - 1960's - 1970's Simulated Annealing (S.A.) - 1983 Tabu Search (T.S.) - 1986 Ant Colony Optimization Algorithm - 1992 Particle Swarm Optimization Algorithm - 1995 Differential Evolution (D.E.) -1997 Harmony Search (H.S.) - 2001 Honey Bee Algorithm (H.B.A.) - 2004 Artificial Bee Colony (A.B.C.) - 2005 ... Battle Royal Optimizat...
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