Weights in neural networks are fundamental parameters that adjust during the training process to minimize the difference between the actual output and the predicted output by the model. These parameters are critical because they directly influence the strength of the input signals as they propagate through layers in a neural network. Each neuron within a network is connected to other neurons via these weights, which are adjusted using optimization algorithms (like gradient descent) based on the feedback received from the loss function. This process, known as backpropagation, allows the network to learn complex patterns from input data, making weights pivotal in determining the network's performance and its ability to generalize from training data to unseen data.
The concept of weights in neural networks has been integral to the field since its inception, with roots going back to the perceptron introduced by Frank Rosenblatt in 1958. However, the use and understanding of weights, especially in deep learning contexts, gained significant momentum in the 1980s with the popularization of backpropagation algorithms.
- Frank Rosenblatt: Introduced the perceptron, an early neural network, laying the groundwork for the concept of weights.
- Geoffrey Hinton, David E. Rumelhart, and Ronald J. Williams: Played a pivotal role in the development and popularization of the backpropagation algorithm in the 1980s, which is essential for adjusting weights in neural networks.