Understanding why we use Neural Networks: When Linear and Logistic Regression Fall Short!

Why choose neural networks over linear or logistic regression? In this video, we dive into the strengths and limitations of these models and explain why neural networks excel at handling complex, non-linear patterns. We'll break down key concepts like polynomial features, decision boundaries, and activation functions, using clear examples, including a deep dive into image recognition. Whether you're new to machine learning or looking to deepen your understanding, this video will provide valuable insights into why neural networks are a powerful choice for advanced tasks.
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Тэги:

#Neural_Networks #Linear_Regression #Logistic_Regression #Machine_Learning #Deep_Learning #Artificial_Intelligence #Non-linear_Hypotheses #Polynomial_Features #Logistic_Function #Sigmoid_Function #Decision_Boundary #Computer_Vision #Image_Recognition #Neural_Network_Layers #Activation_Function #Complex_Patterns #Model_Complexity #Classification_Algorithms #Supervised_Learning #Machine_Learning_Algorithms #AI_in_Image_Recognition #Data_Science #convolutional_neural_networks

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