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Define the problem: The first step is to define the problem that you want to solve with the neural network. This will help you to determine the input and output of the network, as well as the number of hidden layers and neurons in each layer.
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Initialize the weights: The weights of the neural network are the parameters that the network will learn. They are initialized to random values in the beginning.
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Feedforward: The forward pass is the process of propagating the input through the network to the output.The output of each layer is calculated using a function called the activation function.
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compute the loss: The loss function measures the difference between the predicted output of the network and the desired output. The loss function is used to update the weights of the network.
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Backpropagate: The backpropagation algorithm is used to update the weights of the network. The backpropagation algorithm starts from the output layer and propagates the error back through the network.
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Repeat: The steps above are repeated until the network converges, which means that the loss function is no longer decreasing.