Learning Mechanisms in Neural Networks: Backpropagation, Radial Basis Functions, and Computational Models !!
Abstract: Neural networks learn by processing large sets of labeled or unlabeled data, and then using those examples to process unknown inputs more accurately. The learning process is iterative, and involves: Forward propagation: Inputs, weights, and biases are propagated forward Calculation of the loss function: The difference between the actual result and the correct result is calculated Backward propagation: The network determines the changes to make to weights and biases to produce an accurate result Neural networks are inspired by the biological neural networks in animal brains. They are made up of connected units called artificial neurons, which are analogous to biological neurons. Each connection between neurons can transmit a signal to another neuron. Neural networks are powerful tools in computer science and artificial intelligence, and are used for a variety of tasks, including: Computer vision Speech recognition Machine translation Social network f