Techniques Increased Artificial Intelligence Systems: How To Make Artificial Intelligence?

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facilitates our work in everyday life and special requirements artificial intelligence How to produce? What are the techniques and technologies on the basis? In this article we will examine artificial intelligence in-depth.

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How To Make Artificial Intelligence?

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Using artificial intelligence is quite pleasant and practical but the question of how each person is done from the mind. While the technique behind creating an artificial intelligence system is quite complex, it is possible to simply explain this technique. To understand this, you need to focus on human intelligence first. Artificial intelligence was established on impersonating people’s intelligence.

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There are various neurons to process information in our brain and these neurons are in contact with each other. It is called synaps to the port of neurons. Learning takes place in this connection. Computers also need a similar structure to learn and also need it artificial nerve networks called.

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How Does Computer Learn?

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To understand the artificial intelligence systems, it is necessary to first understand how non-smart computer systems work. Computers have been named "Accounter" for the first purpose of calculation, although it was past as computer in our language. Computers 0s and from 1 machines that understand signals. This is a very classic definition but it is necessary to understand the definition well; these signals indicate that the electric current is not found and there is. So there is no electric current when 0, 1 means there is electric current. Here are the commands we give to our computer are converted to the combination of these 0 and 1, and the computer comments signals from this way. For example, "A" is the combination of "01000001". So we press the letter "A" the computer will understand it from the signal "01000001" and fulfill the request.

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Programming also proceeds with a similar logic. We command computer when programming. In the background, this command is reduced to combinations consisting of 0 and 1, and the understanding of the computer is provided.

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It is used for techniques such as artificial nerve networks as a bit more complex systems. For example Home Home devices, games and robotic devices are created with simple AI networks. For example, you may need a simple nervous network to process a image. The nervous network consists of various layers and bonds. An artificial nervous network used to process image, usually input layerTags hidden layers and from the output layer formed. Each layer ensures the processing of the image by fulfilling a specific task. The bond between these layers is weighted and updated in the learning process. This allows the nervous network to make more sensitive predictions.

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Function of layers

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Input Layer:

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This layer takes the image in the form of pixel values. For example, a colorful image in a 256x256 pixel size is transferred to the input of the network as RGB values in each pixel. This data is the raw material that will start processing the network.

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Hidden Layers:

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Secret layers work to learn the important features in the image. These features can be edges, textures or certain patterns. This process is usually performed with convocational nerve networks (CNN). CNN applies filters (kernels) to process data on the image and produces feature maps as output.

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Pooling layers reduce more information on the image and concentrates important information.

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Output Layer:

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The final layer makes the ultimate forecast of the network. For example, this layer can classify "This is a cat" or "This is a car".

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How Does Artificial Intelligence Learn?

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Artificial intelligence systems use strong nervous networks unlike normal computers. We do plenty of practical about that topic when people learn a topic. We have learned exactly thanks to the practicals we do. Artificial intelligence is also trained with large data sets. So a large amount of data is given to the nerve networks in the complex structure, so learning takes place.

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We talked about how a simple AI network works. In complex systems, various activation functions are used to move away from linearity. Let’s explain this with an example: get a cat picture on the image and guess what the private nervous network is. First of all, the feature is removed. Then mathematical processes are done in the secret layer, meaning neurons work information and each is with the next neurona. In the final output layer, prediction is made. If the outcome of the prediction is wrong, for example, if the cat has been predicted as a dog, the network will fix itself and tries to do that error more.

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When training artificial intelligence, we want to guess both certain outputs from it and we provide the right results that these forecasts can compare. In the course of training, the model predicts on numerous examples and improves itself by learning the difference between real results. When it comes to the test stage, the data that the model does not see before. We do not provide the right answer at this stage; we only want to have an estimate. The model’s success on test data shows how well it is trained. If the desired level results cannot be achieved, different data sets are entered into the re-training process using alternative networking architectures or different loss (loss).

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In this way, trainings are created with artificial intelligence systems. We talked about how AI learned in our article, how classical computers learn, and from artificial nerve networks. If you want to learn more about technology, stay tuned.

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