Deep learning of neural networks

The development of technology has raised humanity to unprecedented heights. The fields of medicine, security, education, and other types of care are at their peak. But that is not all. Artificial intelligence is the next big thing in the world of technology and computer science, but to understand it, it’s important to know what it is made of. It is important to know what deep learning is and what an artificial neural network is.

The field of AI technology is extremely advanced and interesting. These two tools, which are used in artificial intelligence, are very effective in solving complex problems and developing even higher standards in science.

It is safe to say that such a mechanism is a transition to a new level of technology. Today’s companies have already realized its importance and have begun to use it in most of their cases. Take Google for example. To learn from its users Google takes help from search engine AI. If you’re looking for something in its search bar, like “laptop”, and after getting results, you click on it, you’ve just taught AI Googles that “laptop” is what you click on. Wondering how it works? Let’s dive deeper and find out.

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What makes deep learning so special is the way that computers (AI) learn in the same way as humans — through trial and error. If you’re wondering if you’ve seen this before, you’ve probably seen it. It is the technology behind voice control applications for devices such as phones, tablets, or TVs. Not so long ago, we got acquainted with self-driving cars, which are also a product of deep learning. There are many things which artificial intelligence did use Deep learning like the recognition of stop signs, and road obstacles that affect driverless cars.

To perform such actions, a computer using deep learning methods requests a large amount of training data (this is the work of neural networks, we will return to this a little later). Technological advances like self-driving cars need thousands of videos and images to recognize every situation in order to be safe. Recent improvements in deep learning have been pushed to the point where it outperforms humans in a certain number of tasks.

As mentioned above, deep learning uses neural networks to perform these tasks. In most cases, AI with deep learning is referred to as a deep neural network. The word deep in this term denotes the layers that are hidden in the neural network.

Deep learning models are trained by getting enough data and neural network data architectures that learn functions directly from the data without manual labor. Neural networks are systems that are interconnected in the same way as our biological neural networks. Such systems are designed to adapt to situational needs. In the first step, the neural networks determine the results for a definite object, then in the second step, the neural networks determine whether the object is the same or not. Neural networks do not recognize objects the way we do, they recognize objects thanks to their own unique set of functions.

One of the most common and popular types of deep learning is known as conventional neural networks, or CNN for short. The input data is combined with the learned features in this process and with the help of 2D convolutional layers, it makes the architecture well suited for handling 2D data. For example, it can be images or sheets of the coordinate plane.

Conventional neural networks operate in such a way that manual feature extraction is no longer necessary. It extracts traits directly from images. Artificial neural networks have automatic feature extraction. Which makes deep learning models ideal for computer vision tasks such as object classification.

CNN learns to detect different features using the number of hidden layers. The complexity of the learned image features increases continuously layer by layer. CNN is exploring different features at every level.

According to sources, there are three most commonly used ways to use deep learning to classify objects:

While a conventional neural network can be thought of as a standard neural network that has been expanded in space using shared weights, there are also several different types.

A recurrent neural network, not an ordinary one, expands in time due to the presence of edges that go to the next time step. And not the next layer at the same time step. This artificial neural network is used to recognize patterns such as speech or text.

And also there is a recursive neural network. This NN system has no timing aspect for the input sequence, but the input data must be processed hierarchically.

When trying to understand what the real benefits of neural networks are in real-life situations, it can be difficult. Among the stock market experts, the popularity of Artificial neural networks is at its peak. “Algorithm Trading” is something that we can apply with these NN systems. It can be applied to financial markets, stocks, interest rates, and various currencies. Neural network algorithms can find undervalued stocks, improve existing stock models, and leverage deep learning.

Since neural networks are very flexible, they can be used to recognize various complex patterns and predict problems. As an alternative to the above example, the NN system can be used for business forecasting, image cancer detection, and face recognition on social media images.

There are real examples not only in neural networks. Some of the following creations that are also used to describe Deep learning are:

With all this information, it’s clear that deep learning and neural networks are highly connected and probably won’t perform well if separated. It is important to know the main takeaway to understand what deep learning is and what neural networks are.

Neural networks transmit data in the form of input and output values. It is used to transfer data using connections. Whereas deep learning is about function transformation and extraction. Which tries to establish a connection between the stimulus and the corresponding neural responses present in the brain. In other words, neural networks are used for natural resource management, process control, transport management, decision making. While the use of deep learning is very high in recognition of speech and image.

Conclusion

To summarize, we can say that deep learning and a neural network complement each other. And also evolve into an even greater technological miracle than it is today. Artificial intelligence is the next step in our era, and the more experience it gains, the more benefit it will bring to society.

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