Neural Networks: How the backpropagation process works

"This meme hilariously represents how the backpropagation process works in neural networks, but let's break it down a little more!

When training a neural network, information flows in two directions:

• Forward pass :

This is where each layer processes the input and passes it on to the next, calculating predictions. Layer n (the last layer) produces the final output, say the predicted class or value.

• Backpropagation :

Here comes the tough part. After the model makes its prediction, it calculates the error (the difference between the predicted value and the actual value). Now, we need to send this error backward through the network to adjust the weights, starting from the output layer and working back toward the input.

The layers' reactions:

• Layer n (the final layer) is yawning because it's tired from working so hard to give a prediction. It's also the first to get the error feedback.

• Layer n-1 is reacting suddenly because backpropagation is hitting it with the error message to update its weights, and this process continues backward.

In essence, backpropagation is like a chain reaction. Each layer gets its turn to adjust its weights based on how much it contributed to the error, but it all starts with that feedback from the final layer.

Without this process, the neural network wouldn't learn, and it would be stuck making the same wrong predictions!"

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