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Nash Equilibrium In Game Theory ~xRay Pixy

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 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisoner’s Dilemma  07:17  Prisoner’s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, it’s a Nash Equilibrium Prisoner’s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...

How to Create Objective Function for Real-Life Problems || PART 08 || ~x...

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Learn how to create an Objective Function for any real-life optimization problem. 00:00 Introduction 00:25 STEPS 02:41 Numerical Example 06:19 Objective Function Verification 07:48 Graphical Representation  08:31 Conclusion

Hyperparameter Optimization || Numerical Example Using PSO || PART 2 ||...

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Learn how to optimize hyperparameters using PSO. Video Chapters: Hyperparameter Optimization 00:00 Introduction 01:30 Problem 02:15 Hyperparameters 03:24 Problem Setup 04:15 Numerical Example Step-By-Step 12:00 Conclusion

Hyperparameter Optimization || Deep Learning + PSO || Hybrid Approach

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Learn how to Optimize Hyperparameters in Deep Learning Using Particle Swarm Optimization (PSO) Video Chapters: Deep Learning + Particle Swarm Optimization 00:00 Introduction 00:59 Deep Learning 03:05 ANN Example 06:55 Deep Learning Applications 07:55 How to use Deep Learning 10:55 Deep Learning Tools 11:15 Hyperparameter Optimization Example 14:04 Hyperparameter 14:22 Conclusion 
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