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Markov Chains || Step-By-Step || ~xRay Pixy

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Learn Markov Chains step-by-step using real-life examples. Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion

Dingo Optimization Algorithm (DOA) Step-By-Step ~xRay Pixy

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Learn the Dingo Optimization Algorithm (DOA) Step-By-Step using Example Video Chapters: DOA 00:00 Introduction 01:42 About Dingo Lifestyle 02:54 Dingo Hunting Methods 03:49 DOA Flowchart 05:15 DOA STEPS 06:14 Initialize Parameters 06:41 Initialize Population 08:06 Fitness Values Calculation 08:31 Main Loop Start 08:46 Hunting Mathematical Models 09:28 Group Attack 11:37 Percussion 12:26 Scavenger 13:02 Dingo Survival Rate 14:24 New Fitness Calculation 15:00 Conclusion 

Mountain Gazelle Optimizer (MGO) Step-By-Step ~xRay Pixy

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Learn the Mountain Gazelle Optimization Algorithm Step-By-Step. Video Chapters: MGO 00:00 Introduction 01:07 About Mountain Gazelle 03:11 Steps for Mountain Gazelle Optimizer  04:48 Initialize Population 05:11 Fitness Calculation 05:33 MGO Main Loop 07:17 TSM Calculation 10:05 MH Calculation 11:12 BMH Calculation 12:25 MSF Calculation 13:00 Add TSM, MH, BMH, MSF 13:35 Update Herd 14:00 Conclusion 

Quantum Cat Swarm Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Learn Quantum Cat Swarm Optimization Algorithm Step-By-Step using Example Video Chapters: QCSO 00:00 Introduction 01:29 Cat Swarm Optimization 06:34 Quantum Cat Swarm Optimization 08:46 Quantum Computing Principles 12:12  What is Quantum Bit (Qubit)? 12:46 Quantum Population Initialization 16:26 QCSO Advantages 17:05 QCSO Applications 17:20 Conclusion ns 17:20 Conclusion

Soft Computing - Neural Networks || Module 2 || ~xRay Pixy

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Video Chapters: Soft Computing Module 2 - Neural Networks 00:00 Introduction 00:39 Topics Covered in Video 01:19 Neural Network Basics 05:12 Problem 01 - Calculate Net Input to Output Neuron 06:33 Artificial Neural Network Terminologies 06:54 Weights 07:59 Bias 08:13 Threshold 08:33 Learning Rate 08:49 Momemtum Factor 09:03 Problem 02 - Calculate Output of Neuron (Y) using Activation Function 09:30 Activation Function 12:42 Problem 02 - Solution 15:25 Neural Network Types 15:38 Simple Neural Network 16:08 Single Feedforward Neural Network 16:41 Multilayer Feedforward Neural Network 17:21 Single Layer Recurrent Network 17:50 Multilayer Recurrent Network 17:52 Perceptron 21:57 Multilayer Perceptron 22:15 Adaline -Its Training and Capabilities 22:59 Conclusion

Find Maxima of Function using PSO Method || Numerical Example || ~xRay Pixy

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Find the maximum value for the objective function using Particle Swarm Optimization Step-By-Step. Video Chapters: Find the Maxima of Function using the PSO Method 00:00 Introduction 02:18 Objective 03:17 Maximization Problem 04:22 Particle Swarm Optimization Steps 05:22 Step 1 - Objective Function 05:30 Step 2 - Position and Velocity Initialization 06:00 Step 3 - Fitness Calculation 07:06 Step 4 - Update Personal Best 07:16 Step 5 - Update Global Best 07:42 Step 6 - Position Update 10:34 Step 7 - Solution Boundary Checking 10:53 Step 8 - New Solution Evaluation 11:31 Step 9 - Update Personal Best 12:12 Step 10 - Update Global Best 13:24 Iteration 2 Start - Position Update 14:45 New Solution Boundary Checking 15:24 New Solution Fitness Calculation 15:48 Update Personal Best 16:32 Update Global Best 17:42 Conclusion Problem: Find the Maxima of the function � ( � ) = � 2 + 2 � + 11 f ( x ) = x 2 + 2 x + 11 � ( � ) = � 2 + 2 � + 11 f ( x ) = x 2 in the range -2<=x<=2 using PSO m...
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