What are the Differences Between Deep Learning and Machine Learning?

Deep learning and Machine learning, both are concepts that fuel Artificial Intelligence. AI is a technology that with time evolves to think more and more like human beings. Deep Learning, in simple terms, is a part of Machine learning that focuses on making AI think on its own with the data that has been fed to the machine.

A Statista report states Machine learning was used for security and encryption requirements by 56% of respondents of a survey. In the second position, respondents using ML algorithms to monitor model performances were in the highest number. Now, as Deep learning is a subset of Machine learning, it is obvious that both ML vs Deep Learning algorithms are coexisting.

Furthermore, in this blog, to have a clear understanding of the difference between Machine Learning and Deep Learning concepts, we are discussing them with facts. So, if you think the blog can be insightful for you, stay with us until the end of this blog.

What is Deep Learning?

To have a clear Deep Learning comparison against ML, let’s understand first that what is Deep Machine Learning? In simple terms, Deep Learning is a part of Machine Learning that focuses on the inspection and analysis part of the entire Machine Learning process. It includes methods such as AI Neural Networks and Representation Learning.

Deep learning is a combination of multiple layers. These three or more layers create neural networks to copy human behavior. Multiple layers help the Deep Learning process in executing tasks with more accuracy.

How does Deep Learning work?

Well, to understand the difference between Deep Learning vs Machine Learning properly, let’s understand how both types of algorithms work. Generally, there are multiple algorithms used in the process of Deep Learning. So, it is crucial to understand them for a clear understanding of the difference between Machine vs Deep Learning.

What do we understand from Neural Networks?

There are neurons in the brain of AI inter-connected with each other. There are three separate layers to group these neurons- Input Layer, Hidden Layer(s), and Output Layer.

1. Input Layer, as the name suggests is the part of entering data into the network. Neurons are categorized into the type of data you are using to feed the server.
2. Hidden Layer(s), Deep Learning uses multiple hidden layers to categorize and process the information according to its type.
3. Output Layer is used to provide the outcome of the data.

In the example that you see in the above picture, we are trying to understand how flight fare is predicted. Well, to simply understand it, we assign dates weightage according to their importance in the deep learning network. And with the historical data of ticket pricing, the Output layer extracts the data that we needed.

What is Machine Learning?

In simple terms, Machine Learning is an automated process that makes a machine learn itself with the help of Big Data and Deep Learning. By using the training data, Machine Learning algorithms can execute tasks without the need for manual programming or data feeding.

Common use cases of Machine Learning are available even around you. Email filtering, personalized UIs, song recommendations by streaming apps are some of the top examples of Machine Learning existing around you. Machine Learning uses the structured form of data to learn automation. You can categorize the data in the initial stage to feed the network. Later on, ML is capable of using the data independently to make decisions without human intervention.

Types of Machine Learning

To understand Machine Learning or Deep Learning differences properly, it is crucial to know which type of data is fed to them? So, let’s understand them separately.

1. Supervised learning is labeled, properly categorized, and structured to ensure ML networks have a clear understanding of the objects they are learning about.
2. Semi-supervised learning has labeled and non-labeled data to provide ML networks hints and patterns so they can solve the rest of the problem on their own.
3. Unsupervised learning is a common part of the Machine Learning evolution that we witness today, especially in the eCommerce sector. Here, ML networks categorize data as they see fit by finding patterns automatically. eCommerce sites use this data to provide customization to customers depending on their shopping behavior.
4. Reinforcement learning refers to the process of ML where the computer is provided with feedback. For instance, if it does the work right, we provide good feedback, if it does the work wrong, we give it negative feedback. This feedback helps machines in following or changing the pattern of processing they are already using.

So, what are the crucial differences between Deep Learning vs Machine Learning?

To conclude, let’s understand these crucial differences in short.

In the end, we hope that this blog will help you in enhancing your ML software development knowledge. You can subscribe to MobileAppDaily for more informative blogs.

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