What is cnn network,dropout,weight initialization techniques, why relu ?

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CNN is a type of neural network commonly used for image recognition. Dropout is a regularization technique to prevent overfitting. Weight initialization techniques are methods to set initial weights in a neural network. ReLU is a popular activation function due to its ability to address the vanishing gradient problem.

CNN (Convolutional Neural Network) is commonly used for image recognition tasks due to its ability to capture spatial hierarchies in data.
Dropout is a regularization technique where randomly selected neurons are ignored during training to prevent overfitting.
Weight initialization techniques like Xavier and He initialization help in setting initial weights effectively for faster convergence and better performance.
ReLU (Rectified Linear Unit) is a popular activation function in neural networks due to its ability to address the vanishing gradient problem by allowing the network to learn faster and perform better.

What is cnn network,dropout,weight initialization techniques, why relu ?

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