Deep Learning Solution
Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. It has networks capable of learning unsupervised from data that is unstructured or unlabelled. It is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain. Some major parts are:
- 1. Artificial Neuron Network: An Artificial Neuron Network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output. An ANN has several advantages but one of the most recognized of these is the fact that it can actually learn from observing data sets. In this way, ANN is used as a random function approximation tool.
- 2. Convolutional Neural Network: A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. A CNN implements series of many steps such as Convolution, Max Pooling, Flattening before connection to an ANN.