How does deep learning work for robots
What is deep learning?
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 automatically complete a task.
In addition, the deep learning algorithms are characterized by scalability based on the available data. On the other hand, flat networks converge, which ultimately reach a performance plateau by providing further examples.
In addition, 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
The entire information technology is based on the binary basic operations. But especially for the interpretation of complex data, such as image files, a fine adjustment of individual properties is necessary. Accordingly, the software must be able to differentiate between individual gray levels and ambivalences by 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 making steady progress. 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 one of the predestined fields of application for deep learning algorithms. As a result, significant advances could also 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
Numerous manual processes in companies can be automated with the help of Robotic Process Automation. However, the use of the technology is very limited and dependent on standardized processes. If a software robot encounters input data that does not correspond to 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. By using modern algorithms for image recognition, machines are given the opportunity to differentiate between cultivated plants and weeds. In this way, pesticides and herbicides can be used selectively. In addition, through the use of deep learning, farmers can also monitor the cultivation of crops and use the chemical supplements where required. This enables the use of pesticides to be reduced in the long term and yields increased. But the technology can also be used profitably for irrigation of the fields or the subsequent harvest.
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