One of the most important branches of artificial intelligence is machine learning, with the help of which machines can learn different concepts and interpret new data using their knowledge. Deep learning algorithms include techniques by which the learning process can be simulated for machines. These techniques are widely used by big companies such as Google, Amazon, Microsoft, etc., and they increase their power day by day.
In other words, whether we like it or not, these companies know us more every day and based on that knowledge, they give us offers that are based on our interests and attract our attention. Of course, this is the least that these companies do 🙂
Usually, deep learning algorithms are run on large sets of data because it simplifies the process of data analysis for data scientists. Since deep learning algorithms have a direct relationship with neural networks, first we compare these two concepts, then we introduce 4 deep learning algorithms; Finally, we describe their applications and how they work.
If you have researched the learning process in the human brain, you have probably heard the name neural networks. In fact, we humans can learn different subjects and use them in our lives by using neural networks that are formed by connecting neurons to each other. Artificial intelligence is also supposed to be inspired by the human brain and eventually be able to simulate a structure similar to the human brain.
Here I intend to note that deep learning algorithms are designed based on artificial neural networks; But a series of points should be considered in the design of these networks. Note that in deep learning, deep neural networks must be used; This means that there must be more than one hidden layer. For more information, you can read the neural networks article.
However, you should note that deep learning algorithms, in addition to deep neural networks, can also use other techniques. By putting these things together (neural networks and other techniques) it is possible to leave the solving of complex problems to machines.
As we said, deep learning algorithms are based on neural networks. Therefore, the use of the word neural network in the following algorithms does not mean that it is independent from deep learning.
A deep learning algorithm should be able to accept a set of data as input, perform processing on them, find relationships between input data, and finally deliver a machine learning model to us. You can see 4 deep learning algorithms in the list below:
This type of neural network is much easier to understand than other neural networks, and for this reason, when we intend to acquire knowledge related to deep learning, we start from learning this type of neural network.
This deep learning algorithm is in the category of feedforward neural networks. Feedforward neural networks mean that the connection between the neurons of this network does not form a cycle. In other words, it can be said that these types of neural networks are not recurrent.
In this type of deep neural networks, the number of input layers should be equal to the number of output layers and the number of hidden layers should be more than one layer. This type of neural network is usually used in image recognition and text translation.
In the image below, you can see an example of a multilayer perceptron:
This deep learning algorithm is directly related to the Boltzmann machine and is a combination of several Boltzmann machines. In fact, the purpose of designing this neural network was to solve the gradient fading problem in Boltzmann machines. Although this type of deep neural networks is practical and solves the problem of gradient fading in Boltzmann machines, this algorithm is rarely used.
A deep belief neural network can be considered a stack of Boltzmann machines. Each layer is connected to the previous layer and the next layer and can transfer data between layers. This type of network is used for image recognition and video recognition.
These types of neural networks are useful for processing ordinal and time series data. Ordinal data is data that is entered into the neural network in a certain order, and at each step it decides what output it should produce in the current step, compared to the previous output. The best example of ordinal data is Google’s recommendation system. When you type a text, the data enters the network and provides us with suggestions for each change from the previous data and the current data.
In this neural network, the output data is connected to the input of the network and this causes repetition and the time (t) increases in each repetition. This type of deep neural network is similar to multilayer perceptron neural networks, with the difference that in multilayer perceptron networks, no recursion takes place.
In this type of deep neural networks, based on the previous data and the current stage, decisions are made to generate new data.
The following image shows a recurrent neural network:
As you can see, this network has a simple structure, but it can be repeated many times and each time, according to the current time, deliver a specific data. The output data is directly connected to the network input and the time (t) is also incremented at each return step. In the image below, you can see the return steps in this network:
Another deep learning algorithm that is most used in image recognition is convolutional neural network. In this algorithm, different parts of an image are examined and then a certain weight is assigned to that part. Finally, after passing the learning period and building a model, it is expected to classify the processed images and be able to recognize the new input image categories.
Convolutional neural network is used to process images that consist of many colors and have more parameters than binary images.
In the image below, you can see a view of the convolutional neural network:
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The mentioned cases are not the only deep learning algorithms and there are more cases where each algorithm can be used in a specific field. In this article, we wanted to introduce some deep learning algorithms to you so that you can have an overview of each one and prepare you for more information.
The best way to understand a subject is to practice and try; So, if you plan to learn about deep learning algorithms in practice and check their behavior with different data, you can use Big Pro1.
It should be noted that for every process you perform, Big Pro1 also delivers the Python file related to that process and you can view that file and get more information.