![cover image](https://wikiwandv2-19431.kxcdn.com/_next/image?url=https://upload.wikimedia.org/wikipedia/commons/thumb/5/54/Feed_forward_neural_net.gif/640px-Feed_forward_neural_net.gif&w=640&q=50)
Feedforward neural network
One of two broad types of artificial neural network / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Feed-forward neural network?
Summarize this article for a 10 year old
SHOW ALL QUESTIONS
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]
This article needs additional citations for verification. (September 2011) |
![](http://upload.wikimedia.org/wikipedia/commons/thumb/5/54/Feed_forward_neural_net.gif/320px-Feed_forward_neural_net.gif)
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them.
Subsequent run of the network on an input image (left):[1] The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a false positive result for sea urchin.
In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.
In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.