In the context of artificial intelligence (AI), particularly in machine learning and neural networks, weights are parameters that determine the strength of the connections between neurons or nodes in a network. They play a central role in learning and prediction. Here’s a breakdown of what weights are and their role: 1. Weights in Neural Networks In a neural network, each connection between two neurons has an associated weight. These weights control how much influence one neuron has on another. When data (input) is passed through the network, the input is multiplied by the weights before it is passed through an activation function. The activation function determines whether or not the neuron will "fire" (i.e., pass information to the next layer). 2. Learning Process During training, the model adjusts its weights based on the errors or differences between the predicted output and the actual output. This process is typically done through backpropagation, where the weights are updated using an optimization algorithm like gradient descent. The goal is to find the optimal set of weights that minimizes the error in predictions, or the loss function. 3. Weight Initialization Proper initialization of weights is crucial to training success. If weights are initialized too large or too small, or if they are the same across all neurons, the network might have difficulty learning effectively. 4. Weights in Different Models In a linear regression model, weights represent the coefficients that determine the relationship between input features and the predicted output. In deep learning models, weights are learned across multiple layers of the network, allowing the model to learn hierarchical features of the data. 5. Biases vs. Weights Alongside weights, there are biases in the network, which are additional parameters that allow the model to shift the activation function’s output, enabling more flexibility in modelling complex patterns. Key Idea: Weights are essentially what the model "learns" during training. They control the influence of input features on the output, and the process of training is about adjusting these weights to improve the accuracy of the model's predictions. In summary, weights in AI, particularly in neural networks, are the parameters that are learned during training and define how data moves and transforms through the network to make predictions.