Overview
Knowledge representation in neural networks is a crucial aspect of artificial intelligence, enabling machines to learn from data and make informed decisions. It involves encoding information through weights and biases, which are adjusted during the training process. Understanding how these component...
Key Terms
Example: Each neuron in a network receives inputs, applies a weight, and passes the result through an activation function.
Example: Higher weights mean the input has more influence on the neuron's output.
Example: Bias helps the model make predictions even when all input features are zero.
Example: Common activation functions include ReLU and sigmoid.
Example: A neural network typically has an input layer, one or more hidden layers, and an output layer.
Example: During training, the network learns from labeled data to improve its predictions.