Explain working of Back Propagation and Convolution Neural Network.

Backpropagation and Convolutional Neural Networks (CNNs) are fundamental concepts in the field of artificial intelligence (AI) and machine learning (ML), particularly in training deep learning models. Each plays a crucial role in enabling models to learn from data and make predictions or classifications. Here is a detailed explanation of both:

Backpropagation:

Backpropagation is a supervised learning algorithm used for training neural networks. It aims to minimize the error between the predicted output and the true output by adjusting the weights of the neural network during the training process. The key steps in backpropagation are:

1. Forward Pass:
   The first step in backpropagation is a forward pass through the network. The input data is passed through each layer of the network, from the input layer to the output layer. Each layer performs a weighted sum of the inputs, followed by the application of an activation function (like ReLU, Sigmoid, or Tanh), producing outputs for the next layer. This process continues until the final output is obtained.

2. Loss Calculation:
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