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Mashinali o‘qitishga kirish Nosirov Xabibullo xikmatullo o‘gli Falsafa doktori (PhD), dotsent, tret kafedrasi mudiri
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Bog'liq Mashinali o\'qitishga kirish 21-ma\'ruza Nosirov Kh HamdamovAyyubxon, ma\'lumotlar tuzulmasi va algaridimlar amaliy ish - 1, Ma\'lumotlar bazasi mustaqil ish -3, Ma\'lumotlarni saralash algoritmlari. Saralash tushunchasi va uni-kompy.info, sun-iy-neyron-tarmoqlarni-umumiy-tasnifi, Mashinali o\'qitishga kirish 1-ma\'ruza Nosirov Kh (1), Asosnoma Quraqov S.A., Haydarqulov Shohzod, OR-5.51.02.02-Elektr atansiyalari tarmoqlari va tizimlari, lab5power point, Kompyuter tarmoqlari va adminstratorlash fanidan test savollari , 8-sinf mavzulashtirilgan testlar, menejment texBiological Neurons
Multiple layers in a biological neural network (human cortex)
The flow of electric signals through neurons.
Biological neuron
Logical Computations with Neurons Warren McCulloch and Walter Pitts proposed a very simple model of the biological neuron, which later became known as an artificial neuron: it has one or more binary (on/off) inputs and one binary output. The artificial neuron simply activates its output when more than a certain number of its inputs are active.
McCulloch and Pitts showed that even with such a simplified model it is possible to build a network of artificial neurons that computes any logical proposition you want. For example, let’s build a few ANNs that perform various logical computations, assuming that a neuron is activated when at least two of its inputs are active.
Artificial Neural Networks The following diagram represents the general model of ANN which is inspired by a biological neuron. It is also called Perceptron. A single layer neural network is called a Perceptron. It gives a single output.
In the figure, for one single observation, x0, x1, x2, x3...x(n) represents various inputs(independent variables) to the network. Each of these inputs is multiplied by a connection weight or synapse. The weights are represented as w0, w1, w2, w3….w(n). Weight shows the strength of a particular node.
b is a bias value. A bias value allows you to shift the activation function up or down.
In the simplest case, these products are summed, fed to a transfer function (activation function) to generate a result, and this result is sent as output.
Mathematically, x1*w1 + x2*w2 + x3*w3 ...... xn*wn = ∑ xi*wi
Now activation function is applied 𝜙(∑ xi*wi)
Activation function The Activation function is important for an ANN to learn and make sense of something really complicated. Their main purpose is to convert an input signal of a node in an ANN to an output signal. This output signal is used as input to the next layer in the stack.
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