Weights & Biases
In an artificial neural network (ANN), weights and biases are learnable parameters that determine the behavior and performance of the network. They are essential for capturing the relationships between inputs and outputs. Let's understand the roles of weights and biases:
1. Weights:
Weights are values assigned to the connections between neurons in different layers of the network. Each connection has an associated weight, which represents the strength or importance of that connection. During the training process, the network adjusts these weights to minimize the error and improve the network's performance.
Each neuron in a given layer receives inputs from the previous layer, and these inputs are multiplied by the corresponding weights. The weighted inputs are then summed, and an activation function is applied to produce the output of the neuron.
The weights are initially assigned random values and are updated iteratively during training using optimization algorithms like gradient descent. The goal is to find the optimal set of weights that minimize the difference between the predicted output and the actual output.
Adjusting the weights allows the network to learn the appropriate patterns and features in the input data, enabling it to make accurate predictions or classifications.
2. Biases:
Biases are additional parameters in ANNs that enable fine-tuning and provide flexibility to the network. Each neuron, except those in the input layer, typically has an associated bias term. A bias is a constant value that is added to the weighted sum of inputs before applying the activation function.
The bias allows the activation function to be shifted, introducing a certain level of bias towards or away from the activation. It helps the network model more complex relationships between inputs and outputs and provides a form of control over the activation level.
Similar to weights, biases are initialized with random values and updated during the training process. They allow the network to learn the optimal activation levels and help generalize well to unseen data.
Together, weights and biases play a crucial role in determining the behavior and performance of an ANN. The learning process involves adjusting these parameters based on the training data, allowing the network to learn and make accurate predictions or classifications on new, unseen data.
