This is a simple feed-forward neural network with 2 inputs, 1 hidden layer with 3 neurons, and 1 output neuron.
1. Input Layer: The starting point where data enters the network. Each input neuron holds a value between 0 and 1.
2. Hidden Layer: These neurons process the information from the input layer through weighted connections.
3. Output Layer: Provides the final result after processing all inputs.
4. Connections: Each connection has a weight that multiplies the value passing through it.
5. Activation: After summing all incoming weighted values, each neuron applies a sigmoid activation function to produce its output.
Adjust the input values and weights to see how the network responds!