CLASS 9, PIC Features, PIC Microcontroller Basics, The Widrow-Hoff, advanced algorithms, AI, LEARN 

CLASS 9, PIC Features, PIC Microcontroller Basics, The Widrow-Hoff, advanced algorithms, AI, LEARN, Widrow Learning Rule,The Widrow-Hoff, delta rule, Marcian Hoff, advanced algorithms, connections, The Widrow-Hoff or Widrow learning rule, also known as the delta rule, is a foundational algorithm in machine learning and neural networks used for supervised learning tasks. Named after its creators, Bernard Widrow and Marcian Hoff, it is specifically designed for training single-layer perceptrons. The rule operates by adjusting the weights of connections between neurons based on the difference between the predicted output and the actual target output. This adjustment, or learning, is proportional to the input values and the error gradient, facilitating the network's ability to learn and improve its accuracy over time. Widrow learning is iterative, with each iteration refining the weights to minimize the error between predicted and actual outputs. This rule plays a significant role in basic neural network training, serving as a precursor to more advanced algorithms like backpropagation used in multi-layer perceptrons. Its simplicity and effectiveness make it a valuable tool in scenarios where computational resources are limited or where the problem at hand can be adequately addressed by a single-layer neural network model.