Machine learning is the process of using algorithms to parse data, learn from it, and then make a decision or prediction. Instead of preparing program codes to accomplish a task, the machine is ‘trained’ using large volumes of data and algorithms to perform the task on its own.
Machine learning is being revolutionized using neural network (NN) algorithms, which are digital models of the biological neurons found in our brains. These models contain layers which are connected like a brain’s neurons. Many applications benefit from machine learning, including image classification/recognition, big data pattern detection, ADAS, fraud detection, food quality assurance and financial forecasting.
As algorithms for machine learning, neural networks include a wide range of topologies and sizes consisting of multiple layers; the first layer (a.k.a. input layer), middle layers (a.k.a. hidden layers) and the last layer (a.k.a. output layer). Hidden layers perform a variety of dedicated tasks on the input and pass it to the next layer until, at the output layer, a prediction is generated.