📚 node [[neural_network|neural network]]
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⥅ related node [[cnns convolutional neural networks]]
⥅ related node [[neural networks]]
⥅ related node [[recurrent neural networks in nlp]]
⥅ related node [[rnns recurrent neural networks]]
⥅ related node [[bayesian_neural_network]]
⥅ related node [[convolutional_neural_network]]
⥅ related node [[deep_neural_network]]
⥅ related node [[depthwise_separable_convolutional_neural_network_(sepcnn)]]
⥅ related node [[feedforward_neural_network_(ffn)]]
⥅ related node [[neural_network]]
⥅ related node [[node_(neural_network)]]
⥅ related node [[recurrent_neural_network]]
⥅ related node [[unsupervised neural networks for detecting fake news]]
⥅ related node [[a mostly complete chart of neural networks]]
⥅ node [[neural-networks]] pulled by Agora
📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 2 - Introduction/Neural Networks.md by @KGBicheno

Neural Networks

Go to [[Week 2 - Introduction]] or back to the [[Main AI Page]]

Learn through a process called [[Back-propagation]]

A collection of neurons is called a layer. A layer takes an input and gives an output.

Any neural network will have one input layer and one output layer. A neural network with more than one hidden layer is called a deep network.

How they work

A graphical representation of how a neural network functions

A neural network processes an input vector to a resulting output vector through a model inspired by neurons and their connectivity in the brain.

The model consists of layers of neurons interconnected through weights that alter the importance of certain inputs over others.

Each neuron includes an activation function that determines the output of the neuron (as a function of its input vector multiplied by its weight vector).

The output is computed by applying the input vector to the input layer of the network, then computing the outputs of each neuron through the network (in a feed-forward fashion).

A modern perceptron

The following perceptron takes two inputs [i0, i1], modified by their own weights [w0, w1] and the target neuron's bias [w2] and are then passed through an activation function, in this case a step function [1], a step function saying if input >= 1, output == 1, else output == 0.

Here's a graphical representation of that perceptron

See [[Perceptron Learning]] for more information on training a neural network.
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📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Neural_Network.md by @KGBicheno

neural network

Go back to the [[AI Glossary]]

A model that, taking inspiration from the brain, is composed of layers (at least one of which is hidden) consisting of simple connected units or neurons followed by nonlinearities.
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