Neural Networks are one particular type of Machine Learning technique. They are a type of artificial intelligence modeled on the brain. There are nodes or artificial neurons that are each responsible for a simple computation. These nodes are networked together with connections of varying strengths, and learning is reflected in changes to those connections. An important characteristic of neural networks is the relationship between nodes. Often, there is an input layer, an output layer, and one or more in between layers (called “hidden layers”), which can result in a model that has a lot of complexity, but may be difficult to interpret.
Neural networks are often used for image analysis, for example, training the algorithm to identify diabetic retinopathy in images of the eye.
Programming languages such as R and Python have a number of different libraries to provide functions for doing neural network analyses.
These two entries (part 1 and part 2) provide a concise explanation of neural networks:
Pokharna, Harsh. “For Dummies - the Introduction to Neural Networks We All Need ! (Part 1).” Medium, TechnologyMadeEasy, 26 July 2016, https://medium.com/technologymadeeasy/for-dummies-the-introduction-to-neural-networks-we-all-need-c50f6012d5eb.
Pokharna, Harsh. “For Dummies - the Introduction to Neural Networks We All Need ! (Part 2).” Medium, TechnologyMadeEasy, 27 July 2016, https://medium.com/technologymadeeasy/for-dummies-the-introduction-to-neural-networks-we-all-need-part-2-1218d5dc043#.iqo9f7tkh.