Neural networks are a type of machine learning algorithm modeled after the structure and function of the human brain. They are designed to recognize patterns and make predictions based on the input data.
A neuron takes the value of the connected neuron and multiplies it with their connection weight. Results of the edges connecting to the next neuron are summed up with the bias. Sum of all the connected neurons and bias value is fed into something called activation function. Activation function transforms the input value and the result is then passed onto the next neurons. The final output is a prediction or a classification based on the input data. Real challenge in the neural network is to find the right weights to get the right results.
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