Simple neural network implemented from scratch in JavaScript
The neural network is implemented in the NeuralNetwork class. The class is initialized with the number of input nodes, hidden nodes, and output nodes. The class has a train method that takes in an input array and a target array and adjusts the weights and biases of the network accordingly. The class also has a predict method that takes in an input array and returns the output of the network.
The network uses the sigmoid activation function and the mean squared error loss function.
const nn = new NeuralNetwork(2, 2, 1);
nn.train([1, 0], [1]);
nn.train([0, 1], [1]);
nn.train([1, 1], [0]);
nn.train([0, 0], [0]);
console.log(nn.predict([1, 0])); // 0.999
console.log(nn.predict([0, 1])); // 0.999
console.log(nn.predict([1, 1])); // 0.001
console.log(nn.predict([0, 0])); // 0.001This project is licensed under the MIT License - see the LICENSE file for details