Neural Network is one of the most importa concepts in artificial ielligence that today plays a key role in areas such as image processing, sound detection and language translation. It may seem complicated at first glance, but if we explain it in a simple language, you will find that the main idea behind the neural network is to simulate human brain function.
A simple definition of neural network
Scieists were inspired by the human brain to design artificial neural networks. In our brain there are billions of neurons that communicate with each other and are responsible for learning and processing information. The system strives to simulate the same process using algorithms and mathematics.
In the simplest definition, the neural network is a system that receives, processing inputs (data), and ultimately produces appropriate output. This processing is done through layers of “syhetic neurons”.
Difference with machine learning and deep learning
Machine learning is a set of ways to teach models based on data. In corast, neural network is one of the machine learning tools that processs the data more layer.
Difference in applications
Simple machine learning models are usually used for lighter issues such as classification or prediction. But neural networks have the power to analyze more complex data such as images, sound and natural language.
Why the neural network is a branch of deep learning
Deep Learning is based on multilayer neural networks. So neural networks can be said to be the main foundation of deep learning.
How is the neural network structure
The following is a definition of the compones of a neural network:
Artificial neurons
A syhetic neuron is similar to brain neurons. This neuron receives input data, performs calculations and sends the result to subseque neurons.
Input layer, hidden, output
- Input layer: He receives data.
- Hidden layer: The main data processing is done in this section.
- Output Layer: The end result provides data such as prediction or categorization of data.
Weights, bias and activation function
- Weights (Weights): They determine the importance of each input.
- Bias (Bias): It is used to adjust the flexibility of the model.
- Activation Function: It determines whether the output of a neuron is active.
Neural network training algorithms
There are differe algorithms to teach a neural network that we will iroduce to some of the best:
Descending gradie and optimization
The descending gradie is one of the most common algorithms to reduce network error. In this method, the weights are adjusted in a step -by -step form to make the model more accurate.
Backupaging
Backpropagation is an algorithm that calculates the network output error and returns back to modify the weights.
Famous Activation Functions (Relu, SIGMAID, SoftMax)
- Relu: Fast and widely used in deep networks
- SIGMOID: Suitable for possible outputs
- Softmax: Application
A variety of neural networks and their architecture
- Perseptron: The simplest type of neural networks with an output layer.
- FEEDFORWARD: The information only flows from the input to the output and does not return.
- Return (RNN): It is used for sequeial data such as text or time series, because it has short -term memory.
- Canvashan Network (CNN): For the processing of images and videos.
- Basic radial function network (RBF): It is used for the approximation and approximation of mathematical functions.
- Encoder-Decoder Model: Used in translating language or producing text.
- Model Network: A combination of a few smaller networks that each have a specific task.
Network applications in the real world
In the real world, neural networks have many applications such as face recognition, ideification of objects and image processing in industry, medicine, sports, and more. Tools such as Google Transylite and translator tools are also used to translate texts.
Conclusion
Neural networks are a powerful tool in the world of artificial ielligence that let us better analyze data and build smart systems. If you are a newcomer, it is advisable to first learn basic concepts such as machine learning and then go for neural networks and deep learning. Understanding these structures is a gateway to the fascinating world of artificial ielligence.
