Deep Learning Vs Machine Learning

Did you know that by 2025, machines are expected to perform more tasks than humans in the workplace? Machine learning helps computers make decisions by learning from previous data. They help develop algorithms and statistical models. These algorithms and statistical models are then trained to use large datasets.

On the other hand, deep learning requires multilayered neural networks to help them analyze complex patterns in the data. The global deep-learning market is expected to reach $18.16 billion by 2023, growing at a CAGR of 41.2% from 2018 to 2023. In this blog, you will learn more about deep learning vs machine learning. 

What is Machine Learning?

Before we move on to the comparison of machine learning and deep learning, let us understand what is machine learning. Machine learning is a subset of Artificial Intelligence involving statistical techniques. It enables computer systems to improve performance. These computers can learn various algorithms from the already existing data. They can perform tasks such as predicting outcomes and recognizing patterns when new data comes.

There are various machine learning algorithms. It is carried out by simple statistics regardless of the complexity of the algorithm. 

To sum up machine learning:

• It is a point where statistics and computer science meet. 
• It can learn from the previous data and apply the same algorithm to new data.
• It has two categories: supervised learning and unsupervised learning. 
• It uses simple statistical methods. 

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What is Deep Learning?

Deep learning is a subfield of machine learning. It analyzes data logically and can take place through both supervised and unsupervised learning. Deep learning applications are inspired by the working of the human brain. They use artificial neural networks (ANN), a layered structure of algorithms. 

Deep learning is used in almost every field today. It has captured the core of all technology, such as Satellites, Amazon Alexa, Automated Driving, etc. For example, a deep learning model could be trained to recognize the difference between a dog and a cat by analyzing features like the shape of ears, fur texture, and facial features. 

It can be used for various tasks like natural language processing, image and speech recognition, and decision-making. 

To sum up deep learning:

• It is a subfield of machine learning.
• The algorithms have a mathematical complexity.
• It takes place through both supervised and unsupervised learning.
• It is inspired by the working of the human brain.

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Difference Between Machine Learning and Deep Learning

Now let’s look at the difference between machine learning and deep learning.

Machine Learning	Deep Learning
It is a subset of Artificial Intelligence.	It is a subset of Machine Learning.
It uses structured data.	It uses artificial neural networks (ANN).
It does not require huge data points.	It requires millions of data points.
It uses automated algorithms to predict future actions.	It uses ANN to pass data through processing layers.
It learns from past data to make future predictions.	It resolves various machine-learning issues.
Its training is done by using the Central Processing Unit (CPU).	Its training is done by using Graphics Processing Unit (GPU).
Human intervention is needed to get the required outcomes.	It is self-reliant.
Machine learning applications can be set up in no time, as the algorithms are easy to make.	Deep learning applications take time to be set up, as it requires a large amount of data.
It does not take a lot of training time.	It requires a long period of training as the data is huge.
Feature engineering is done by humans.	The ANN enables it to do feature engineering on its own.
It is easy to perform and can be made possible on standard computers.	Its algorithms are very complex to build. Therefore it requires powerful hardware and resources.
The results are easily explained.	The results are not easy to explain.
The ML model can resolve easy and a little bit complicated issues.	Deep learning models can resolve very challenging issues.
Used in banks, offices, etc.	Used in automobiles, etc.
It is not very accurate.	It is extremely accurate.

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Why is Deep Learning Not So Famous?

As mentioned above, deep learning can be self-reliant and can function on its own. So why is it not famous in today’s time? It is because of the following reasons that it is not taking over the technological monopoly. 

• It requires a huge amount of data. 
• It requires substantial computing power. 
• It is a complex and computationally intensive field.
• It requires a lot of expertise and resources.
• Its performance is highly dependent on the quality and quantity of training data.

How Is Deep Learning Related to Machine Learning?

Deep learning is a part of machine learning. It is a type of machine learning that employs different neural networks to learn data representations. It uses neural networks to learn complex patterns in data. While that is the case, machine learning embodies various methods for coaching models to make forecasts or choices based on information. 

For example, to understand how is deep learning related to machine learning, we can talk about the field of Natural Language Processing (NLP). Another example is in the field of computer vision. Computer vision is a field that deals with enabling machines to interpret and understand digital images and videos.

Both machine learning and deep learning belong to the same platform, i.e., Artificial Intelligence. Both of them can perform tasks like image recognition and speech recognition. 

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Conclusion

After a comprehensive differentiation of deep learning vs machine learning, we hope you understand what it is. Both machine learning and deep learning can positively influence a lot of technological advancements in various fields. The choice between deep learning and machine learning depends on the specific problem, available data, and the resources for training and deployment.