Explained In A Minute: Neural Networks from switchnet neural network model for forward and inverse scattering problems page 14 Watch Video

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In simple language, weight is a value that can determine how much influence the input will have on producing an output signal. Thus, the weight controls the strength of the signal connecting 2 nodes. Weight is often symbolized as w.<br/><br/>#NeuralNetwork #ArtificialIntelligence
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In this series, we will get to know the basic concepts of artificial neural networks. We will get to know several terms such as input layer, hidden layer, and output layer. We will also compare artificial networks with human neurons.<br/><br/>#NeuralNetwork
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Here is a simple artificial neural network. There are 3 nodes on the input screen, 5 nodes on the hidden screen, and 2 nodes on the output screen. Each node is connected to each other via connection lines. Based on the previous series, the input value of the spots on the hidden screen is the sum of all signals from the spots on the input screen, plus the bias value.<br/><br/>This input value will be processed within the node to produce the output value, y1. So, this y1 value will be the value that will be sent to each node on the next screen. In this series, we will calculate the output value of this node.<br/><br/>#NeuralNetwork #ArtificialIntelligence
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