IEEE Sir Padampat Singhania University

Prof (Dr). A.R.Garg explained about the following:

Supervised learning: input and corresponding output is given and it produces correct output for new inputs. Reinforcement learning: Animal learning. . Unsupervised learning: We give inputs as training, find the structure in which the observed inputs belong. Applications: Handwriting recognition, OCR, speech recognition, speaker recognition/verification, text classification, security: face detection and recognition, computer vision, diagnosis, adaptive control, fraud detection, database marketing, spam filtering, games, financial prediction. Unsupervised learning: clustering, embedding, compression. Problems of interest for computer scientists and engineers: Pattern Classification, Clustering, Function Approximation, Predicting/forecasting, Optimization, Content Addressable, Memory. Huge simplification: A neuron computes a weighted sum of its inputs and fires when that sum exceeds threshold. Hebbian learning: Synaptic weights change as a function of the pre and post synaptic activities. Speciality of biological neural system are: massive parallelism, Distributed representation, and computation, Learning ability, Generalization ability, Adaptivity, Inherent contextual information processing, Fault tolerance, low energy consumption. Neural Network examples like: Banking by prediction, big advertising to optimize their ad choice. Artificial Neural Network: Information processing paradigm inspired by the biological nervous system. Activation function (Which transforms neurons input to output) and its features like Squashing effect and simple calculations. Decision boundaries: which divides feature space by drawing a hyperplane across it. Discriminant function: returns different values on opposite side. A perceptron consists of input values, weighted bias, a weighted sum, and activation function.