Machine learning is a subset of artificial intelligence associated with creating algorithms that can change themselves without human intervention to get the desired result – by feeding themselves through structured data.
Deep learning is a subset of machine learning where algorithms are created and function similarly to machine learning, but there are many levels of these algorithms, each providing a different interpretation of the data it conveys. This network of algorithms is called artificial neural networks. In simple words, it resembles the neural connections that exist in the human brain.
You have a collection of photos of dogs and cats. Suppose you need to identify images of dogs and cats separately using machine learning algorithms and deep learning neural networks.
HTo help the machine learning algorithm classify images into collections according to two categories (dog and cat), it needs to present these images first. But how does the algorithm know which one is which?
The answer to this question is the availability of structured data, as described above in the definition of machine learning. You simply mark the images of dogs and cats in order to determine the characteristics of both animals. These data will be sufficient for training a machine learning algorithm, and then he will continue to work on the basis that they understand the markings and klassificeret millions of other images of animals both on the grounds that he had studied earlier.
Deep learning neural networks will use a different approach to solve this problem. The main advantage of deep learning is that it doesn’t necessarily need structured / tagged image data to classify two animals. In this case, the input data (image data) is sent through different levels of neural networks, and each network hierarchically determines the specific features of the images.
This is similar to how our human brain works to solve problems – running queries through different hierarchies of concepts and related questions to find the answer.
After processing the data through different levels of neural networks, the system finds appropriate identifiers to classify both animals by their images.
So, in this example, you can see that the machine learning algorithm requires labeled/structured data to understand the differences between images of cats and dogs, study the classification, and then draw a conclusion.
On the other hand, the deep learning network was able to classify the images of both animals from data processed in the layers of the network. This did not require any labeled/structured data, as it relied on different outputs processed by each layer, which were combined to form a single way of classifying images.
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