Neural Ai
Neural Ai – The concept of Neural Networks is not difficult to understand for the average person. Whatever your personal situation, in this lesson we will explain the concept of Neural Networks (NN) or Neural Networks and formally explain it. Now let’s take a look at the application of the neural network.
As you know We will try to keep it simple and clear. so that you can easily understand and evaluate the concepts We will investigate the following issues:
Neural Ai
A neural network is a mathematical model based on the biological network that makes up the human brain. Neural networks are not dependent on pre-written computer software. But they can learn and improve their performance over time.
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A neural network is made up of cells or cells known as neurons. These neurons are connected through a connection known as a synapse. Through a synapse, a neuron can send signals or information to other neurons nearby. Sensory neurons are able to receive signals, process them, and identify next steps. The process continues until the signal is emitted.
Keep in mind that Neural Networks is a branch of Artificial Intelligence. Right now, you’ll be working with systems that try to mimic the way people do things. There are many modern applications of neural networks, including:
Computer Vision: Because it’s impossible to write a program that knows everything that’s going on. The only way is to use a neural network to provide over time. The computer automatically learns new things based on what it has learned.
Recognition/Synchronization: Can be used to search the photo library for familiar faces and speak the same. used in criminal investigations
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Natural Language Processing: A system that helps computers recognize human spoken language by learning and listening to it little by little over time.
How can we implement the ecosystem in real life? The first step is to find a neural network representing neurons. We assign a real number to each neuron. This exact number represents the signal that the nerve has.
The output of each neuron is calculated as a random function. This function takes the sum of all additions of those neurons.
Therefore, both neuron and synapse have a fixed weight that changes as learning progresses. This scale controls the strength of the signal that a neuron passes through the synapse to the next neuron. Neurons are often organized into categories. Different categories can change multiple input types. The signal passes through different floors, including hidden floors, until it exits.
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A perepron is a simple model of a neuron demonstrating the operation of an artificial neural network. Perceptron is a machine learning algorithm developed in 1957 by Frank Rosenblatt and first used on an IBM 704.
How the perceptron works is shown in Figure 1 in this example. The perceptron has three inputs, x1, x2, and x3, and one output.
The importance of these resources is determined by the relative weights w1, w2, and w3 assigned to these resources. The output can be 0 or 1 depending on the input load.
The output is 0 if the sum is below the specified level, or 1 if the output is above the specified level. These parameters can be real numbers and parameters for neurons.
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This cognitive function explains the basics of neural networks and is a great starting point for learning neural networks.
Now I will look at the details of the neural network. But I will split it into 2 parts because I want to keep this tutorial as simple as possible.
Don’t forget the general example. This time we didn’t just have 3 tools. But there are also tools. As you know, the formula will become:
This is very different from what we’ve been used to. But if we use a function like this The output can be any number, however, we want the result to be a number between 0 and 1.
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What we’re going to do is pass these weights to a number function to return a value between 0 and 1. What would the function look like? Yes, that’s the sigmoid function! This is also known as the target line.
In the sigmoid function The worst negative value approaches zero. and the most positive value approaches 1 and increases exponentially to zero.
Suppose we want a neuron to fire when its negative output exceeds a certain level, i.e. below this level. Nerve cells do not work. above level will work Now we call this tree bias and apply it.
Bias is a measure of how high the load is before the nerve fires.
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Our review is similar to the one above with only two parts, but neural networks can actually be multi-layered. And that is what we will explore in the next tutorial on artificial intelligence.
Around 2012, researchers at the University of Toronto used deep learning for the first time to win ImageNet, a popular computer image recognition competition. by beating the best techniques with a large margin. For those involved in the AI industry, this is a big deal. because of computer vision Discipline in helping computers understand visual patterns It’s one of the most challenging parts of intelligence.
And naturally just like any other technology that made a huge impact Deep learning has been the target of many hype. Deep learning has come online in the latest artificial intelligence revolution. and companies and organizations started using it to solve various problems Many companies are starting to transform their products and services using deep learning and artificial intelligence. Others try to use deep learning to solve problems beyond their capabilities.
On the other hand, the media often write stories about AI and deep learning that distort reality and are written by people who don’t really understand how the technology works. Other, lesser-known brands use AI terminology. To collect feedback and increase advertising revenue These have contributed to the wave of deep learning.
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And like the other concepts mentioned, deep learning faces challenges. Six years later, many experts believe that deep learning is being overestimated. This will eventually wane and could lead to yet another AI winter, when interest and support for artificial intelligence dwindles. will happen greatly
Other leading experts agree that deep learning has broken barriers. And some of the leading researchers in deep learning have contributed to some of the most important advancements in the field.
But according to renowned scientist and deep learning researcher Jeremy Howard, “deep learning is superfluous” is a bit overkill. Educate yourself exclusively online. It has a lot of experience in teaching AI to people without a computer science background.
Howard rejected many of the arguments against deep learning in a speech at the USENIX Enigma conference earlier this year. Each video clearly explains what deep learning does and doesn’t. And it gives you a clear picture of what to expect from the website.
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Many people think that deep learning never happened. And as soon as it appears, it disappears.
“What you really see in deep learning today is the result of decades of research,” Howard said. And decades of research have finally come to the cutting edge,” Howard said.
The concept of neural networks, an essential part of deep learning, has been around for decades. The first neural networks began in the 1950s.
But thanks to decades of research and access to information and computing resources in recent years. The concept of deep learning was moved from the research lab and found its way into practical use.
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“After doing all this work Eventually people got to work,” Howard said. “You should expect [Deep learning] increases instead of disappears.”
To be honest, some people might use multiple words to describe different AI techniques in the same way. And the misuse of AI terminology has created confusion and uncertainty in the industry. Some people say that deep learning is just another name for machine learning. While others call it on par with other AI tools such as support for machine learning (SVM), random forest, and regression.
But deep learning
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