Difference Between Machine Learning and Deep Learning

Machine learning and deep learning has become an integral part of every career. Over the years, computers have been trying to make accurate predictions with as little human intervention as possible.

Machine learning and deep learning are two such attempts in the field of artificial intelligence that aim at improving computer efficiency and understandability. 

Machine Learning vs Deep Learning 

The main difference between machine learning and deep learning is that machine learning comprises deep learning as one of its subsets. While machine learning is an evolved version of artificial intelligence, deep learning is an evolution of machine learning. 

Machine Learning vs Deep Learning

Machine learning focuses on the application of data and algorithms to copy the way through which human beings acquire information.

Some of the fields employing machine learning are medicine, email filtering, speech recognition, and computer vision. In addition, machine learning is an evolved version of artificial intelligence.

The outputs of machine learning are usually numerical such as a classification of score. 

Deep learning is based on artificial neural networks with representation learning. The process of deep learning involves the use of multiple layers in the network.

Deep learning is also known as deep structured learning. These layers are allowed to be heterogeneous in deep learning to ensure efficiency and understandability.

There are a wide variety of deep learning architectures. Deep learning comprises millions of data points.

Deep learning tends to solve complex problems by employing data and algorithms. 

Comparison Table Between Machine Learning and Deep Learning 

Parameters of ComparisonMachine Learning Deep Learning 
Data PointsMachine learning comprises thousands of data points.Deep learning has lakhs of data points. 
Function The main objective of machine learning is to stay competitive and learn new things.Deep learning functions to solve complex issues. 
OutputsThe outputs of machine learning are usually numerical such as a classification of score. The outputs of deep learning include numerical values as well as free form elements.
Complexity Machine learning is less complex, and thereby, easier to understand than deep learning. Deep learning is a complex process. 
Set-up timeMachine learning requires less set-up time. Deep learning requires more set-up time. 

What is Machine Learning?

Machine learning is a subset of artificial intelligence and computer science.

The main objective of machine learning is to focus on the application of data and algorithms to copy the way through which human beings acquire information.

Machine learning algorithms build a model called training data that is based on sample data. 

There are several practical applications of machine learning. Some of the fields employing machine learning are medicine, email filtering, speech recognition, and computer vision.

Machine learning is effectively used in these fields as it is difficult to develop conventional algorithms otherwise. Machine learning is known as predictive analytics in the field of business.

Therefore, machine learning is a process that employs data and algorithms to yield reliable results. Machine learning lays emphasis on the development of computer programs that analyses the data and uses it for themselves.

In addition, machine learning is an evolved version of artificial intelligence. The outputs of machine learning are usually numerical such as a classification of score. 

Some prominent applications of machine learning are in agriculture, astronomy, banking, citizen science, computer vision, information retrieval, insurance, handwriting recognition, marketing, medical diagnosis, and search engines.

There are certain limitations to machine learning such as the inability to deliver expected results. Additionally, machine learning may be subject to different data biases. 

What is Deep Learning?

Deep learning refers to a branch of machine learning. Another name of deep learning is structured deep learning.

There are a wide variety of deep learning architectures. Some of these are deep neural networks, deep reinforcement learning, deep belief networks, and convolutional neural networks.

Some practical applications of deep learning are in the field of computer vision, natural language processing, bioinformatics, material inspection, speech recognition, and drug design.

The process of deep learning involves the use of multiple layers in the network. These layers are allowed to be heterogeneous in deep learning to ensure efficiency and understandability. 

Deep learning comprises millions of data points. The outputs of deep learning include numerical values as well as free form elements.

Deep learning tends to solve complex problems by employing data and algorithms. Deep learning can be constructed by employing the greedy layer by layer method.

Deep learning methods have a vital practical application in unsupervised learning tasks. 

The most convincing case of deep learning is large scale speech recognition. Other areas of operation of deep learning are visual art processing, image recognition, natural language processing, and customer relationship management.

However, some deep learning techniques may display problematic behaviour. 

Main Differences Between Machine Learning and Deep Learning 

  1. While machine learning consists of thousands of data points, deep learning consists of millions of data points. 
  2. The main objective of machine learning is to stay competitive and learn new things. In contrast, deep learning functions to solve complex issues. 
  3. Machine learning requires less set-up time. On the other hand, deep learning requires more set-up time. 
  4. Machine learning is less complex, and thereby, easier to understand than deep learning. 
  5. The outputs of machine learning are usually numerical such as a classification of score. In contrast, the outputs of deep learning include numerical values as well as free form elements.
Difference Between Machine Learning and Deep Learning

Conclusion

Therefore, machine learning and deep learning vary on several grounds. Some prominent applications of machine learning are in agriculture, astronomy, medical diagnosis, and search engines.

In contrast, some areas of operation of deep learning are visual art processing, natural language processing, and customer relationship management.

Machine learning is effectively used in these fields as it is difficult to develop conventional algorithms otherwise. 

Both machine learning and deep learning are pivotal to analyze large amounts of data and algorithms for making accurate predictions.

They are ideal concepts introduced by the field of artificial intelligence to make machines as smart as human beings. While these are the only points of similarity, both machine learning and deep learning have several peculiar differences. 

References

  1. https://en.wikipedia.org/wiki/Deep_learning#Applications 
  2. https://en.wikipedia.org/wiki/Machine_learning#Applications 
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