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Neuron Mesh Networks
Hamroyev Babirjon Bakhridtdinovich
Asia international University , " General Technician Department of Sciences
teacher
Abstract :
This in the article neuron netted networks artificial of intellect
important from directions one as is studied . Neuron networks a person brain and of
neurons work principles based on created big in volume information again work and
analysis in doing important role plays In the article neuron of networks history ,
structure , activation functions and study processes in detail seeing will be released
. Deep deep learning approaches and neuron of networks different in the fields ,
including medicine , automobile industry , finance technologies and another in the
fields application also information about given In the future neuron networks
development directions , including hybrid models and quantum computers with
depends achievements about thought maintained. This article neuron networks about
wide to understanding have to be and their modern in technologies place about deep
information get for important source being service does
Keywords:
Neuron netted networks, Artificial intellect, Deep learning (Deep
Learning), Activation functions, the machine learning (Machine Learning), Back
spread algorithm (Backpropagation), Convolutional neuron networks (CNN),
Recursive neuron networks (RNN), Optimization Algorithms of Gradient decrease
(Gradient Descent), Quant computers , Hybrid models, Image Familiar, Time
Sequence, Artificial neuron models .
Introduction
Neuron laced networks is one of the important areas of artificial intelligence, they
are inspired by the working principles of the human brain and neurons. This
technology is important in processing and analyzing large amounts of data. Neural
networks are also the main part of machine learning (Machine Learning) and allow
to achieve high results in solving complex tasks.
Neuron laced networks history
neural networks began in the middle of the 20th century. The first artificial neuron
model was developed by McCulloch and Pitts in 1943. They created a mathematical
model of artificial neurons that gave a simple representation of how neurons work.
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After that , the Perceptron created by Rosenblatt in 1958 was an important step in
the study of neural networks. Since the 1990s, many new neural network techniques
have been developed, including backpropagation and deep learning approaches.
Today, deep neural networks serve as the basis of many artificial intelligence
systems
.
Neuron of networks structure
Neural networks mainly consist of three parts:
1. Input layer: It is the first part of the network that receives the initial data.
This information is usually in the form of numbers .
2. Hidden layers: These layers perform data processing. The complexity of
the network is determined by the number of hidden layers and neurons in them. 3.
Output Layer: The final step in the network that provides the final decision or output
based on the input data.
Activation functions
Activation functions are important in neural networks because they determine the
activity of a neuron and control the output of the network. The most common
activation functions are:- Sigmoid: Keeps the output between 0 and 1 .
- ReLU (Rectified Linear Unit): Converts negative values to 0, leaving
positive values unchanged.
- Softmax: Used to interpret results as probabilities.
Study process and optimization
learn
complex
data
.
This
process
consists
of
several
steps:
1. Forward propagation: The input data moves through the network and the final
result is generated.
2. Error calculation: The difference between the network output and the actual
result is measured. This difference is called "error".
3. Backpropagation: Based on the error, the weights of the neurons are
recalculated and the error is reduced by updating the network.
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4. Optimization algorithms: To speed up and improve the learning process,
various algorithms, such as Gradient Descent, are used.
Deep study and complicated networks
Deep learning (Deep Learning) refers to neural networks consisting of many hidden
layers. Deep networks show high performance in analyzing complex data sets, such
as image recognition, voice command recognition, and natural language processing.
Examples of deep learning models:- Convolutional Neural Networks (CNN): Mainly
used in image analysis.- Recursive Neural Networks (RNN): Effective when dealing
with time series and correlated data.
Neuron networks to apply fields
Neural
networks
are
currently
used
in
various
fields:
1. Medicine: Neural networks are used to improve disease detection and treatment
processes. 2. Automotive: Self -driving cars have the ability to see their surroundings
and
make
decisions
through
artificial
neural
networks.
3. Games : Neural networks are being used to play complex strategic games and win
players. 4. Financial technologies: Provides high accuracy results in financial
forecasting, investment and stock trading .
In the future neuron networks development
Further development of neural networks is expected in the future. Currently, several
directions
are
being
developed
to
improve
neural
networks
:
-
Hybrid models: Models combining traditional algorithms and deep learning
methods. - Quantum computers: Quantum computer technologies are being
developed
to
increase
the
computing
power
of
neural
networks.
Summary
Neural networks are of great importance in the development of modern technology
and artificial intelligence. Their ability to solve complex problems and process large
amounts of data will enable many future technological advances. New technologies
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and methods related to neural networks are being studied and their applications more
expansion is showing .
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