What is Deep Learning: Fundamentals of Neural Network Software

What is Deep Learning: Fundamentals of Neural Network Software

What is Deep Learning

The evolution of technology has brought humanity to heights, as we have never seen before. The areas of work in medicine, security, learning, and the provision of other types of aid have reached a maximum. But it doesn’t stop there. Artificial intelligence is the next great revolution in the world of technology and computing, but to understand it, it is important to know what it is. It is essential to understand what deep learning is and what the artificial neural network means.

The field of AI technology is extremely advanced and exciting. These two tools that are being used in artificial intelligence are very powerful in solving complex problems and in developing even higher standards in science.

This type of mechanism can be safely said to be a transition to the next level of technology. Today’s companies have already recognized its importance and have started using it in most of their cases. Take Google as an example. Google uses search engine AI to learn from its users. If you search for something in your search bar, for example, a “laptop,” and after you get the results, click on it, you have just taught Google AI that a “laptop” is what you clicked—wondering how it works? Let’s dive deeper and find out.

Understanding AI Deep Learning

What is unique about Deep learning technology, which is a technique for computers (AI) to learn like humans – by trial and error. If you wonder if you’ve seen it before, you probably have. It is the technology behind applications such as voice control of devices such as phones, tablets, or television. Not long ago, we have known autonomous driverless cars, which is also a product of deep learning. With the help of DL, artificial intelligence recognizes stop signs, pedestrians, and other roadside obstacles that could cause a disaster.

To perform such actions, a computer using deep learning techniques requests a large amount of training data (this is the job of neural networks, we’ll get to that a little later). Technological achievements like driverless cars need thousands of images and videos to recognize every situation to make it safe. Recent improvements in Deep learning have been brought to the level where it outperforms humans on several tasks.

How does it work?

As already mentioned slightly, what is used in deep learning to perform such tasks is neural networks. Most of the time, deep learning AI is referred to as a deep neural network. The word deep in this term means the layers that are hidden in the neural network.

Deep learning models are trained by obtaining a sufficient amount of data and neural network data architectures that learn characteristics directly from the data without manual labor. Neural networks are systems that are connected just like our biological neural networks. These types of systems are created in a way that adapts to the needs of the situation. Once neural networks identify the results for a particular object, the next time NN systems can determine whether it is the same object or not. Neural networks do not recognize objects in the same way that we do, but they recognize objects through their own unique set of characteristics.

Artificial neural networks

One of the most common and popular types of what is used in deep learning is known as conventional neural networks or CNN for short. It combines the learned characteristics with the input data and uses 2D convolutional layers, making this architecture very suitable for processing 2D data. For example, they can be images or sheets of coordinated planes.

Conventional neural networks operate in such a way that there is no longer a need for manual feature extraction. Extract features directly from images. Artificial neural networks have automated feature extraction that makes deep learning models perfect for computer vision tasks like object classification.

CNN learns to detect different characteristics using hidden layer numbers. Each number in the hidden layer increases the complexity of the characteristics of the learned image. CNN’s learn different characteristics of each segment.

Deep Learning Examples

According to the sources, there are three most widely used ways of using deep learning to perform object classification:

  • Transfer learning. The learning approach is used primarily in deep learning applications. It is done by having an existing network and adding new data to previously unknown classes. This way, it is much better to save some time because instead, the amount of image processing is reduced. It allows you to categorize only certain objects instead of going through all the different objects until you find the correct one.
  • I am training out of nowhere. This is mainly used for new applications that are going to have a large number of output categories. It starts by gathering a large number of tagged data sets and designing a network architecture that will learn the characteristics. While transfer learning can take up to hours or minutes, this method takes a little more time – days to weeks to train.
  • Feature extraction. It is not as popular as the methods mentioned above, but it is still commonly used. This is a method that is used for a more specialized deep learning approach. It uses the network as a feature extractor. Since the layers in conventional neural networks are tasked with learning certain characteristics from images, it is also possible to extract these characteristics and make them as input to a machine learning model.

What are other types of neural networks?

While the conventional neural network could be thought of as the standard neural network that has expanded through space using shared weights, there are also a few different types.

A recurring neural network, instead of the conventional one, stretches over time by having edges that feed at the next time step instead of the next layer at the same time step. This artificial neural network is used to recognize sequences, for example, a voice signal or a text.

There is also a recursive neural network. This NN system has no temporal aspect to the input stream, but the input has to be hierarchically processed.

Neural networks in action

It can be difficult when it comes to an understanding of what the real benefits of neural networks are in real-life situations. Artificial neural networks are very popular with stock market experts. With the help of NN systems, “algorithmic trading” can be applied, which can be applied to financial markets, stocks, interest rates, and various currencies. Neural network algorithms can find undervalued actions, improve existing action models, and use deep learning to find ways to optimize the algorithm as the market changes.

Since neural networks are very flexible, they can be applied to various complex pattern recognitions and predict problems. As an alternative to the example above, the NN system can be used to forecast businesses, detect cancer from images, and recognize faces in social media images.

Deep learning in action

It’s not just neural networks that have real-life examples. Deep Learning can also be described as some of the following creations:

  • Virtual assistants.
  • Chatbots or service bots.
  • She personalized shopping and entertainment.
  • Imagine the coloration (uses algorithms to recreate true colors in black and white images)

What are the key differences between DL and NN?

With all this information, it is clear that Deep Learning and Neural Networks are strongly connected and probably will not work well when they are separated. To understand what deep learning is and what neural networks are, it is essential to know the primary way to carry out learning.

Neural networks transmit data in the form of input values ​​and output values. They are used to transfer data through the use of connections. While Deep Learning is related to the transformation and extraction of characteristics that try to establish a relationship between the stimulus and the associated neural responses present in the brain, in other words, neural networks are used for natural resource management, process control, vehicle control, decision making. In contrast, Deep Learning is used for automatic speech recognition, image recognition, etc.


In short, Deep Learning and the Neural Network compete with each other and will develop into an even greater technological wonder than today.

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