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"Efficient Reinforcement Learning Through Evolving Neural Network Topologies" (PDF). Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002). Kenneth O. Stanley; Bobby D. Bryant & Risto Miikkulainen (2003). "Evolving Adaptive Neural Networks with and without Adaptive Synapses" (PDF).
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.
An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network.
Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
The classical example of unsupervised learning in the study of neural networks is Donald Hebb's principle, that is, neurons that fire together wire together. [8] In Hebbian learning, the connection is reinforced irrespective of an error, but is exclusively a function of the coincidence between action potentials between the two neurons. [9]
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. Artificial ...
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.