Feedforward neural network - Wikipedia
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers.[2] Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops,[2] in contrast to recurrent neural networks,[3] which have a bi-directional flow. Modern feedforward networks are trained using the backpropagation method[4][5][6][7][8] and are colloquially referred to as the "vanilla" neural networks.[9]
Feedforward neural network - Wikipedia Jump to content From Wikipedia, the free encyclopedia Type of artificial neural network This article needs additional citations for verification . Please help improve this article by adding citations to reliable sources . Unsourced material may be challenged and removed. Find sources:   "Feedforward neural network"  –  news   · newspapers   · books   · scholar   · JSTOR ( September 2011 ) ( Learn how and when to remove this message ) Part of a series on Machine learning and data mining Paradigms Supervised learning Unsup
saved by
related reading
- Recurrent neural network - Wikipediaen.wikipedia.org
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- Hopfield network - Wikipediaen.wikipedia.org
- Backpropagation - Wikipediaen.wikipedia.org
- Recurrent neural networks - Scholarpediascholarpedia.org
- Backpropagation | Brilliant Math & Science Wikibrilliant.org
- Bayesian Neural Networkscs.toronto.edu
- Yes you should understand backprop | by Andrej Karpathy | Mediumkarpathy.medium.com
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- nn-notes.pdfboris-hanin.github.io
- Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
- [2212.13345] The Forward-Forward Algorithm: Some Preliminary Investigationsarxiv.org