Human learning is based on experience. It is true that a wealth of experience has a positive influence on people’s learning behavior. Deep learning also makes use of this principle. In principle, artificial intelligence is one of the relevant future technologies of the coming years. As a sub-area of this area, deep learning is also
Human learning is based on experience. It is true that a wealth of experience has a positive influence on people’s learning behavior. Deep learning also makes use of this principle. In principle, artificial intelligence is one of the relevant future technologies of the coming years. As a sub-area of this area, deep learning is also gaining importance for further development. In the following, you will find out how deep learning works and what it can be used for.
What is deep learning?
Deep learning is a special method of information processing and can be incorporated into the research field of machine learning classification. Neural networks are also used to imitate the functioning of the human brain. For the training of this artificial intelligence, large amounts of data are needed and analyzed. To generate a profound learning effect, the developers orientate themselves on the functioning of the human brain. Accordingly, the system can access the information that is already available and the neural network. This approach enables already learned skills to be enriched and linked with new content. Overall, this enables a profound and long-term learning process to be mapped. The technical basis is artificial neural networks, which are continuously re-linked during the learning process.
The machine can make its own decisions through deep learning. In addition, the system can create its own forecasts and question decisions made. Decisions that have already been made are confirmed or changed during a new review. In addition, no human intervention is necessary as part of the learning process. As a result, the technology is particularly suitable for applications that are based on large databases. Here the system can derive patterns and models and support people in their work.
How does deep learning work?
Deep learning enables machines to improve and learn new skills without human action. The system extracts patterns and calculation models from existing data. These findings can then be correlated with data and linked to a corresponding context. The machine can ultimately make decisions based on the context gained.
The continuous questioning of the decisions made helps ensure that the information links are given certain weightings. Confirmed decisions increase the weighting, while revised decisions decrease the weighting. This approach results in numerous stages between the input and output layers, the so-called intermediate layers. These intermediate layers and their links are ultimately also responsible for the output. The name of the technology already indicates the presence of these layers. The part of the name “deep” or “deep” refers to the hidden layers of the neural network. While classic neural networks only have two or three hidden layers,
One of the most popular neural networks is the convolutional neural network. Such a convolution network convolves the learned features with input data in 2D convolution planes. With the help of this architecture, typical 2D data can then be processed. In addition, there is no manual extraction of features, which is used to classify the data. All features are learned by training the convolution network with appropriate data. Another special feature is the functionality of the individual layers in the network because each layer takes on a different function when recognizing the data. The more layers are used, the more detailed the knowledge gained.
What is the difference between deep learning and machine learning?
Basically, both deep learning and machine learning belong to the thematic area of artificial intelligence. However, deep learning is a branch of machine learning. However, machine learning already differs from deep learning in the initial workflow. With machine learning, the relevant features have to be extracted manually. The software then uses these extracted features to create a model. A modern deep learning workflow, on the other hand, extracts the required features automatically and without human intervention. Accordingly, this is an end-to-end learning process in which the software learns to complete a task automatically.
In addition, the deep learning algorithms are characterized by scalability based on the available data. Et, In contrast, flat networks converge, which ultimately reaches a performance plateau by providing further examples.
Else, the results of deep learning networks improve as the size of the database increases. This means that the neural network will continuously improve with an expansion of the available database.
Application examples for deep learning
Complete information technology is based on binary basic operations. But especially for the interpretation of complex data, such as image files, fine-tuning of individual properties is necessary. Accordingly, the software must be able to differentiate between individual gray levels and ambivalences using the software. The software has made enormous progress in recent years, particularly in the field of image and video recognition. But voice and speech recognition is also advancing continuously. In the following, we will show you in which areas the technology can be used.
Disease detection by means of image evaluation
Image recognition is considered one of the predestined fields of application for deep learning algorithms. As a result, significant advances could be made in medicine. The technology can be used to examine x-rays or CT images for abnormalities. Thanks to the learned pattern recognition, the software can identify disease patterns quickly and precisely. The decisions made are based on extensive data sets. These data sets often contain several million images of corresponding disease patterns. As a result, the precision of a diagnosis is usually higher than that of human decisions.
Functional expansion for software robots at Robotic Process Automation
With the help of Robotic Process Automation, numerous manual processes in companies can be automated. However, the use of technology is very limited and dependent on standardized processes. If a software robot encounters input data that does not meet the required standard, it fails because of these data types. Deep learning is preparing to break through this limitation. The technology can evaluate and process input data so that automated processing using RPA is possible. In addition, the software can also use the results of Robotic Process Automation to make a complex decision.
Realizing efficiencies in agriculture
The use of artificial intelligence can also revolutionize classic agricultural cultivation. The use of modern algorithms for image recognition gives machines the ability to differentiate between crops and weeds. In this way, pesticides and herbicides can be used selectively. In addition, using deep learning, farmers can also monitor the cultivation of crops and use chemical supplements where required. This enables the use of pesticides to be reduced in the long term and yields to be increased. But the technology can also be used profitably for irrigation of the fields or the subsequent harvest.
Deep learning as a branch of artificial intelligence is the focus of further research. In particular, the options for automating complex processes offer decisive added value. The increasing development of neural networks also enables complex workflows to be mapped. This approach creates added value, particularly in areas where machines are superior to humans. Technology supports people in medicine in making diagnoses. This allows clinical pictures to be identified and treated at an early stage. In agriculture, too, the use of technology offers advantages and improves the entire agricultural sector. In addition, automation using software robots offers further advantages.