Deep learning vs machine learning

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Today, many terms related to artificial intelligence, machine learning and deep learning are widely used in the business context, especially when it comes to making correct predictions and analyzing data.
The production processes of today’s companies demand efficiency and automation. The market has grown, therefore, companies are increasingly focusing on the use of chatbots and other programs and systems to improve logistics, productivity and customer service, with a significant impact also on the presence and visibility of brands. In this regard, artificial intelligence, machine learning and deep learning are becoming increasingly important.
Some IT professionals determine that, using AI, machines can interpret a variety of data to achieve objectives with greater flexibility, accuracy and efficiency.
Artificial intelligence is one of the most striking advances, as it enables machines to learn to predict certain types of behavior, based on data analysis. For this reason, it finds application in a variety of businesses, exploring machine learning capabilities on several fronts, such as:

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This article explains deep learning vs. machine learning and how they fit into the broader category of artificial intelligence. Learn about the deep learning solutions you can build in Azure Machine Learning, such as fraud detection, facial and voice recognition, sentiment analysis, and time series forecasting.
By using machine learning and deep learning techniques, you can compile machine systems and applications that perform tasks normally associated with human intelligence. These tasks include image recognition, speech recognition and language translation.
Now that you have some general information about machine learning and deep learning, let’s compare the two techniques. In machine learning, the algorithm must be told how to make an accurate prediction; to do so, it must gather more information (e.g., by performing feature extraction). In deep learning, on the other hand, the algorithm can obtain information on how to make an accurate prediction through its own data processing, thanks to the artificial neural network structure.

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Today, many terms related to artificial intelligence, machine learning and deep learning are widely used in the business context, especially when it comes to making correct predictions and analyzing data.
The production processes of today’s companies demand efficiency and automation. The market has grown, therefore, companies are increasingly focusing on the use of chatbots and other programs and systems to improve logistics, productivity and customer service, with a significant impact also on the presence and visibility of brands. In this regard, artificial intelligence, machine learning and deep learning are becoming increasingly important.
Some IT professionals determine that, using AI, machines can interpret a variety of data to achieve goals with greater flexibility, accuracy and efficiency.
Artificial intelligence is one of the most striking advances, as it enables machines to learn to predict certain types of behavior, based on data analysis. For this reason, it finds application in a variety of businesses, exploring machine learning capabilities on several fronts, such as:

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One offshoot of AI was machine learning, where the computer extracts knowledge through supervised experience. This used to involve a human operator helping the machine learn by providing it with hundreds or thousands of training examples and manually correcting its mistakes.
Unlike with machine learning, deep learning is less subject to supervision. It involves, for example, the creation of large-scale neural networks that allow the computer to learn and “think” for itself without the need for direct human intervention.
Deep learning “doesn’t really look like a computer program,” says Gary Marcus, a psychologist and AI expert at New York University. In this regard, he comments that ordinary computer code is written following very strict logical steps. “But what you find in deep learning is something different. There is no set of instructions saying, ‘If one thing is true, do this other thing,'” he says. Instead of being based on linear logic, deep learning is based on theories about how the human brain works.  The program is made up of nested layers of interconnected nodes. After each new experience, it learns by rearranging the connections between the nodes.