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Deep Learning vs. Machine Learning is a topic that most people neglect and are not familiar with. It is because Deep Learning and Machine Learning look the same. But behind the scenes, both terminologies are incredibly different from each other.
Many researchers and scientists predict that AI will rule the world of technology very soon. But here arises, how? What is the characteristic of AI applications that make them superior to other algorithms? Here we are going to answer all these questions.
Many people don’t know the difference between deep learning and machine learning. So here, we are going to discuss the ins and outs of deep learning vs. machine learning and their definitions.
What is Machine Learning?
If we relate machine learning and artificial intelligence, then machine learning is simply a branch of AI. In machine learning, such applications are created that don’t need to be programmed again and again. They can learn from their experience and have the feature of the auto program.
Definition
Machine learning is a type of AI where we study specific computer algorithms that don’t need to be programmed regularly. Once they are programmed, they can update themselves through the data collected during past experiences.
Example
Examples make things easy to understand. So, here is an example that will help you to understand machine learning more quickly. Nowadays, most cell phones have the feature of voice and face recognition.
Let’s discuss how sound or face recognition works. First, we record our sound or show the cell phone our face through a camera. The algorithm inside the phone saves this data. Whenever the phone uses a facial or vocal recognition feature, it compares the data collected with the previously saved information.
Uses
Machine learning is used in almost every field. From business to medicine, everywhere we can see the use of machine learning. As we know, machine learning is a branch of AI, so it is a significant part of every AI program.
Doctors use machine learning for the diagnosis of different complex diseases. The company managers use it to check the productivity and growth of their company. Moreover, machine learning also plays an essential role during security inspections.
Potential Applications
We know that machine learning has its applications throughout modern technology. From the discovery of new materials to biomedical research, almost every AI program involves machine learning. Machine learning is just like a backbone to artificial intelligence.
What is Deep Learning
Deep learning was first invented by a Russian mathematician named Alexey Ivakhenko in the 1960s. He created some neural networks that functioned properly. These neural networks have only two layers, while nowadays, we can find neural networks with 10+ layers.
When we place the machine learning versus deep learning, both look the same. We study both machine learning and deep learning under the same field, i.e., Artificial Intelligence. Many researchers say that deep learning is a subfield or subset of machine learning.
Definition
Deep learning is a program that uses neural networks to function correctly. The neural networks have multiple layers to collect data from the raw input. More layers of neural networks mean more extraction of data and getting more precise results.
Example
Deep Learning has few examples as compared to machine learning. It is because deep learning requires a lot of data to function effectively. Google has a voice recognition feature, which is supported by deep learning algorithms.
Moreover, when you search for something on Google or any other search engine, you get some search suggestions. These suggestions are provided by the deep learning algorithm installed in the search engine.
Uses
The uses of deep learning are narrower than machine learning. You can use deep learning for image detection, sound recognition, and also for image segmentation. The neural networks in deep learning help in all these processes.
Potential Applications
If you are a Netflix or a youtube user, you can easily understand the potential applications of deep learning. While you are watching a movie on Netflix or any video, you get some suggestions. You may have noticed that you mostly like the videos from the suggestions.
The reason is that these suggestions are based on your previous search and watch history. The neural networks of deep learning algorithms go through your past searches and suggest videos. So, deep learning works with the help of previously saved data, just like machine learning.
Difference
There are many similarities between machine learning and deep learning; they are still different in many aspects. Here we have given some details with each significant difference between the two types of algorithms.
Human Intervention
In machine learning, Human intervention is more as compared to deep learning. In machine learning, we have to reprogram the machine for every new update or change. But deep learning is entirely different.
We can take the example of facial recognition. We know that the lines of our faces change with time. If facial recognition is done by machine learning, the machine can not detect your face after some months. It is because the machine needs to be reprogrammed for the changes in your face.
If the same case is handled with deep learning, the results are the opposite. Deep learning auto learns about the changes in your face and keeps detecting without any problem. The auto-learn feature of machine learning is not as good as deep learning.
Hardware
The hardware of deep learning is much faster and efficient than machine learning. It is because deep learning involves complex calculations in significantly less time. Mainly, GPUs are used for deep learning, while machine learning can also run on the CPU.
Time
Deep learning takes more time as compared to machine learning. The reason is apparent that deep learning requires huge and complex calculations to be solved. Machine learning is much faster than deep learning and can take only a few seconds to some hours.
Data Modeling
Data modeling of deep learning is very complex and time taking. Deep learning can take time from a few hours up to weeks for one problem. Due to this effort, the results of deep learning are more precise than machine learning.
Approach
The approach of both types of systems is also entirely different. Machine learning solves the problem in two steps, while deep learning takes only one step. But still, machine learning can give you results quicker than deep learning.
For instance, if we want to check the number plates of cars in any area. Machine learning would take two steps to solve our problem. First, it would identify the required objects and then recognize them. In comparison, deep learning would give us the result in just one jump.
Applications
We have already discussed the applications of both deep learning and machine learning. Another application of deep learning includes automatic cars, which don’t require a driver to drive them. Machine learning includes the checking of spam emails and weather forecasts.
Frequently Asked Questions (FAQs)
Is deep learning the same as machine learning?
We can not say that machine learning and deep learning are the same, but to some extent, they are. Machine learning is a branch of artificial intelligence, and deep learning lies under Machine learning. Or we can say that deep learning is a subfield of machine learning.
Which is better: deep learning vs. machine learning?
After comparing machine learning vs. deep learning, we knew that deep learning is a subfield of machine learning, so it gets narrower. Deep learning has a more specific function, and it takes a longer time to train. Moreover, deep learning is trained on the GPU, while you can train machine learning on a CPU.
Will deep learning replace machine learning?
Deep learning is a sub-branch of machine learning, so it works more efficiently than machine learning. But deep learning has a drawback, that it needs a lot of data to work correctly. Otherwise, we can’t get the desired results.
What is AI vs. ML vs. deep learning?
These three terminologies are straightforwardly linked with each other. Machine Learning is a branch of Artificial Intelligence, while deep learning is a subfield of Machine Learning.
Is AI the same as ML?
Up to some extent, AI and Ml are the same, but we can not say that. AI makes the different machines, while machine learning develops specific programs that run the machine. ML programs don’t need to be updated explicitly and can learn from previously stored data.
Is all AI machine learning?
No, because AI involves any machine that performs a human task. But machine learning includes those programs that learn on their own, just like humans. Every machine can not perform tasks like the human brain.
Conclusion
The above article was all about deep learning vs. machine learning. A machine learning engineer has a monthly income between 100,000 and 166,000 dollars. Never miss a chance to become a machine learning engineer if you have an interest in this field.
In conclusion, we can say that deep learning will be a significant part of technology in the future. Machine learning can not compete with the efficiency and preciseness of deep learning results.
