Artificial Intelligence and Consciousness - Part 2
An Artificial Neural Network, the primary constituent of Machine learning and Deep Learning, consists of the neurons, i.e. nodes grouped into Input Layer, Hidden Layer and the Output. The input layer provides the problem while the output layer classifies or finds the probability (like detecting the correct image or recognizing faces or texts). It is the hidden layer that is most interesting. The hidden layers are used in feature engineering to detect more and more features progressively as we move towards the output. This is done through processes called forward propagation and back propagation. Simply put, the layers are connected to each other and provide input to each other and each input being associated with a suitable weight and accompanied by a bias. The weights are first chosen randomly and then the network is trained on a set of data. More the training data the better it is, as it may minimize the problem of overfitting esp. when there are too many features. The input s...