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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

Fireworks Algorithm For Optimization || Step-By-Step || ~xRay Pixy

Fireworks Algorithm For Optimization


Learn Fireworks Algorithm For Optimization || Step-By-Step ||
Video Chapters: Firework Algorithm For Optimization 00:00 Introduction 00:55 About Fireworks 04:07 Fireworks Algorithm Steps 05:41 Set Off Fireworks 08:11 Calculate the Total Number of Spark 10:02 Sparks Location Calculation 14:00 conclusion

Firework Algorithm For Optimization Key Points
It is a Swarm intelligence Based Metaheuristic Algorithm. We can use Fireworks Algorithm to solve complex optimization problems in real life. The Fireworks Algorithm is basically inspired by the explosion process of Fireworks in real life. Fireworks Algorithm mimics this Fireworks explosion behavior to find out the optimal solution.
The fireworks algorithm simulates a simple process. 1. Initialize the population for (N) fireworks. 2. Evaluate fireworks performance using an objective function. 3. Set Off N fireworks. 4. Calculate the number of sparks each firework yield and their location. 5. Evaluate new location quality using an objective function. 6. Check the stopping condition. 7. Keep the best solution and select the (n-1) location for an explosion in the next iteration. 7. Display the best solution found.

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