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 What is the role of weights and bias in a neural network?

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This is a question best explained with a real-life example. Consider that you want to go out today to play a cricket match with your friends. Now, a number of factors can affect your decision-making, like:

  • How many of your friends can make it to the game?
  • How much equipment can all of you bring?
  • What is the temperature outside?

And so on. These factors can change your decision greatly or not too much. For example, if it is raining outside, then you cannot go out to play at all. Or if you have only one bat, you can share it while playing as well. The magnitude by which these factors can affect the game is called the weight of that factor.

Factors like the weather or temperature might have a higher weight, and other factors like equipment would have a lower weight.

However, does this mean that we can play a cricket match with only one bat? No – we would need 1 ball and 6 wickets as well. This is where bias comes into the picture. Bias lets you assign some threshold which helps you activate a decision-point (or a neuron) only when that threshold is crossed.


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