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Deep Learning vs Machine Learning

Deep Learning vs Machine Learning

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Deep Learning and Machine Learning both are the subfields of Artificial Intelligence. Both domains have grabbed great attention from the last few years. If you want to learn a comparison between Deep Learning vs Machine Learning, then you are in the right place. Just read this article thoroughly. Nowadays knowing a comparison between Deep Learning vs Machine Learning is not as complex. Pretty simple examples are described here to make the mind clear.

Deep Learning vs Machine Learning:

Deep Learning: Deep learning is a subbranch of Machine Learning which takes part by utilizing various levels of algorithms. In deep learning, each layer provides a unique interpretation of the given data. This works in the form of a network. That is why it is also called artificial neural networks (ANN). Its functionality is the same as the human brain. The real usage of this approach is solving complex problems and calculations. In such cases, hierarchies and layers are properly defined in accordance with the theme of computation.

Machine Learning:  Machine Learning is directly associated with artificial intelligence. It works based on algorithms that perform autonomous tasks accurately without the involvement of human beings. Machine Learning algorithms need labeled data. Labeled data is the data that is marked by human experts and mostly used for training purposes. So, prior to implementing the Machine Learning model, algorithms are trained by that labeled set of data. Therefore, Machine Learning is not used for complex queries.

Example:

A common example to understand Deep Learning vs Machine Learning is to identify dogs and cats from a given set of images. The identification is process is carried out both by Deep Learning and Machine Learning. To learn about top machine learning books click here.

How Deep Learning Will Tackle this Problem?

As we have already discussed that Artificial Neural Networks do not require any sort of structured or labeled data. In deep Learning images are processed through the input layer and after passing through various layers, a hierarchical network will evaluate some parameters for those images. Neural Networks perform work just like a human brain. Here deep learning algorithms become capable to distinguish between “Cats” & “Dogs” after processing the data from several layers. A single way of image classification is performed because the output from different layers is combined at a single forum. We present a new concept of machine learning here.

How Machine Learning Will Handle this Issue?

The Machine Learning algorithm will classify the images into two classes as “dogs” and “cats” by using the labeling approach. As we have discussed earlier that Machine Learning algorithms required structured or labeled data. For little bit complex problems, there is a requirement of one or more experts that first label the data, and then the Machine Learning Model is established based on that labeled/target class. Based on the label provided, the ML model will classify the images of animals. You can search for more about machine learning here.

Summary of Deep Learning Vs Machine Learning:

These two subsets of Artificial Intelligence directly relate with data, to get useful insights. As a comparison between Deep Learning vs Machine Learning, Deep Learning requires more data. Deep Learning is used for complex problems. It works on the phenomenon of hierarchies and layers. Whereas, Machine Learning Algorithms are used for simpler tasks, requires fewer data. Sometimes requires experts for labeling the data.

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