In the context of machine learning and neural networks, “weights” and “parameters” are terms that are often used interchangeably, but they can have slightly different meanings depending on the context:

  1. Weights:
    • Weights are the core components of a machine learning model that are learned from the training data.
    • In a neural network, weights are the values that are adjusted during training. They determine the strength of the influence one neuron has on another. For instance, in a simple neural network, weights are applied to the inputs and summed up to produce an output through an activation function.
    • The process of learning in neural networks involves adjusting these weights based on the error of the model’s predictions compared to the actual outcomes (known as the loss). This adjustment is typically done using an optimization algorithm like gradient descent.
  2. Parameters:
    • Parameters generally refer to all the learnable parts of the model, which include weights and biases.
    • Besides weights, biases are another form of parameter in neural networks. A bias is an additional parameter added to the sum of weighted inputs that allows the activation function to be shifted left or right. This helps the model in a way that it can better fit the data.
    • In more complex models, like those with convolutional layers (used in image processing) or recurrent layers (used in time series or language processing), parameters can include more than just the weights and biases of the neurons. They can also encompass things like the filters in convolutional layers or the gates in LSTM (Long Short-Term Memory) units.

In summary, weights are a subset of parameters, specifically referring to the coefficients applied to input data in a model. Parameters encompass all the learnable elements of the model, including weights and biases. The goal of training a machine learning model is to find the optimal set of parameters (weights and biases) that minimizes the difference between the predicted and actual outputs, often measured by a loss function. The number of parameters in a model is a key factor in determining its complexity and capacity to learn from data.

Notes:

from ChatGPT