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Machine Learning and Deep Learning: Features and Applications

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The designations “machine learning” and “deep learning” are most often used in the same context, as interchangeable fragments. However, the design features and, accordingly, the capabilities of each technology differ. In this concept, the separation continues in the field of application: if machine learning is used to solve business problems in e-commerce, then deep learning technology even allows you to manage vehicles without the participation of an operator. This will help you better understand machine learning company.

Machine Learning: Fundamentals and Scope

Machine learning is the main component of artificial intelligence based on learning algorithms. The main function of the technology is to find common patterns in the presented data in order to predict the likely actions in new conditions with new data. The peculiarity of the technology lies in the ability of the system to “independently” learn and improve by processing new data sets and re-applying the experience already gained. The ability of the system to learn from its own mistakes allows you to take into account changes and produce more accurate forecasts every time.

From a technical point of view, the mechanism of action of machine learning technology is based on algorithms. In turn, the latter are usually divided into supervised algorithms, unsupervised algorithms, and semi-supervised algorithms. The latter kind of algorithms are widely used for speech identification, voice translation or content classification. At the same time, the role of the operator is minimal: the client needs to enter a set of data, for example, voice the word and click on the translation function.

Supervised algorithms require labeled data with clear labeling of functional values. Practical problems that supervised algorithms can solve include simple processes of classifying and separating information.

Machine learning models with the function of “independent” decision making in a specific environment are classified as deep learning.

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What is special about deep learning?

At the heart of deep learning, algorithms are used as subjects that receive privileges after performing the correct actions. With the help of conditional interactivity and feedback, the system can find the right solutions in a specific environment and apply a behavioral model in a specific context. In practice, the technology is used to fill a computer game with bots or to supervise robotic vehicles.

Since deep learning and machine learning are interrelated and contain many of the same software components, they are often not distinguished from each other. While the two models have different potential and capabilities.

While machine learning uses algorithms and static data as a piece of architecture for predictive analysis, deep learning uses neural networks. The latter function in a similar way to the human brain. At the same time, unlike a living organism, neural networks are able to find illogical connections between fragments. This is especially true when dealing with different sources of conflicting data.

Compared to machine learning, deep learning allows you to get better performance when working with a huge amount of data. The main advantage of a neural network is the ability to reuse already prepared sets of algorithms to perform new tasks.

To make an accurate prediction, neural networks use a huge amount of information, also known in the IT environment as “big data”, which is analyzed using iterative algorithms.