Morten Hjorth-Jensen
189 lines
7.6 KiB
HTML
189 lines
7.6 KiB
HTML
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<a class="navbar-brand" href="NeuralNet-bs.html">Data Analysis and Machine Learning: Elements of machine learning</a>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs001.html#___sec0" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs002.html#___sec1" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
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<a name="part0003"></a>
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<h2 id="___sec2" class="anchor">Artificial neurons </h2>
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The field of artificial neural networks has a long history of development, and is closely connected with
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the advancement of computer science and computers in general. A model of artificial neurons
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was first developed by McCulloch and Pitts in 1943 to study signal processing in the brain and
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has later been refined by others. The general idea is to mimic neural networks in the human brain, which
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is composed of billions of neurons that communicate with each other by sending electrical signals.
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Each neuron accumulates its incoming signals,
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which must exceed an activation threshold to yield an output. If the threshold is not overcome, the neuron
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remains inactive, i.e. has zero output.
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<p>
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This behaviour has inspired a simple mathematical model for an artificial neuron.
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$$
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\begin{equation}
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y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u)
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\tag{1}
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\end{equation}
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$$
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Here, the output \( y \) of the neuron is the value of its activation function, which have as input
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a weighted sum of signals \( x_i, \dots ,x_n \) received by \( n \) other neurons.
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<p>
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<li><a href="._NeuralNet-bs000.html">1</a></li>
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<li><a href="._NeuralNet-bs001.html">2</a></li>
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<li><a href="._NeuralNet-bs002.html">3</a></li>
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<li><a href="">...</a></li>
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<li><a href="._NeuralNet-bs013.html">14</a></li>
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