(Image taken from Scientific American, Unveiling Deep Learning’s Hidden Layers) -The Input Layer’s function is to process all input information and pass it on to the Hidden Layer. A hidden layer is made up of multiple layers that filter data and apply a different transformation on the input data so that it can be used by the Output Layer. -The Output Layer of an ANN, which receives data from the previous layers and transmits the designed data. Summary Analogy: Input Layers are like researchers. They gather, analyze, and interpret all the necessary raw data for a research project. Hidden layers are like a vintner. They extract the best quality grapes from which to make wine. The Output Layer is similar to corporate secretaries. They sometimes receive instructions and messages from various callers, such as clients, employees, and business people, and inform their bosses. Learning how the Neural Networks Learn. ANN can function in the same way as the human brain and can learn the same way that we learn. Howard Rheingold, an American critic and teacher, is well-known for his work on the political, social and cultural implications of modern technology. He stated that the neural network is not an algorithm. It is a type of technology that has weights that can be adjusted to make it learn. It is taught through trials. Source: https://www.goeduhub.com/It’s a fact that the neural network can operate and improve its performance after “teaching” it but it needs to undergo some process of learning to acquire information and be fami
