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In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.
The course, originally launched in 2018, is designed and organized by the University of Helsinki and learning technology company MinnaLearn. [2] The course includes modules on machine learning , neural networks , the philosophy of artificial intelligence, and using artificial intelligence to solve problems.
There are two main types of neural network. In neuroscience, a biological neural network is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
While training extremely deep (e.g., 1 million layers) neural networks might not be practical, CPU-like architectures such as pointer networks [91] and neural random-access machines [92] overcome this limitation by using external random-access memory and other components that typically belong to a computer architecture such as registers, ALU ...
An artificial neural network (ANN) combines biological principles with advanced statistics to solve problems in domains such as pattern recognition and game-play. ANNs adopt the basic model of neuron analogues connected to each other in a variety of ways.
Hinton taught a free online course on Neural Networks on the education platform Coursera in 2012. [47] He joined Google in March 2013 when his company, DNNresearch Inc., was acquired, and was at that time planning to "divide his time between his university research and his work at Google".
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]