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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
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BAT ALGORITHM || PYTHON CODE || ~xRay Pixy

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Learn Bat Algorithm Implementation in Python. Video Chapters: Bat Algorithm 00:00 Introduction 00:42 Bat Algorithm Key Concepts 01:58 Bat Algorithm Pseudocode 02:35 Objective Function 02:49 Parameters 03:09 Python Code 06:30 BA Main Loop Start 12:30 Result The Bat Algorithm is a nature-inspired optimization algorithm developed by Xin-She Yang in 2010. It is based on the echolocation behavior of bats. Bats use echolocation to detect prey, avoid obstacles, and navigate in the dark. The algorithm simulates this behavior to find optimal solutions in complex optimization problems. Applications: The Bat Algorithm has been used in various fields, including engineering design, image processing, data mining, and robotics, for solving complex optimization problems. PYTHON CODE: import numpy as np # Define the objective function  def objective_function(x):     return np.sum(x**2) # Initialize the bat population def initialize_bats(n_bats, dim, lower_bound, upper_bound, f_min, f_m...
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