What’s the difference between machine learning and deep learning, and why are more people choosing machine learning

What is the difference between machine learning and deep learning? Let’s find out from this article.

The target

In this article, we compare deep learning with machine learning. We’ll get to know them one by one. And we’ll talk about how they’re different in various ways. In addition to the comparison between deep learning and machine learning, we will also examine their future trends.

Deep learning and machine learning

I. What is machine learning?

Usually, to achieve artificial intelligence, we use machine learning. We have several algorithms for machine learning. Such as:

• Find-S

• Decision trees

• Random Forests

• Artificial Neural Networks

In general, there are three types of learning algorithms:

1. Supervise machine learning algorithms to make predictions. In addition, the algorithm searches for patterns in the value labels assigned to data points.

2. Unsupervised machine learning algorithms: No labels associated with data. Moreover, these ML algorithms cluster the data. In addition, he needs to describe its structure and make complex data seem simple and logical for analysis.

3. Enhanced machine learning algorithms: We use these algorithms to select actions. And, we can see it based on each data point. After a while, the algorithm changes its strategy to learn better.

What is deep learning?

Machine learning is only concerned with solving real-world problems. It also requires some ideas from artificial intelligence. Machine learning uses neural networks designed to mimic human decision-making abilities. ML tools and techniques are two narrow subsets of the main focus on deep learning. We need to apply it to solve any problem that requires thinking — human or man-made. Any deep neural network will consist of three layers:

• The input layer

• Hidden layer

• Output layer

We can say that deep learning is the latest buzzword in machine learning. That’s one way to do machine learning.

3. Deep learning vs machine learning

We use machine algorithms to parse data, learn data, and make intelligent decisions from it. Fundamentally, deep learning is used to create artificial “neural networks” that can learn for themselves and be judged intellectually. We can say that deep learning is a sub-field of machine learning.

4. Machine learning vs. deep learning

A. Data dependency

Performance is the main difference between the two. When the amount of data is small, the deep learning algorithm performs poorly. That’s the only reason DL algorithms require a lot of data to be perfectly understood.

We can see that the algorithm prevails in this artificially created scenario. The picture above summarizes the situation.

B. Hardware dependency

In general, deep learning relies on high-end devices, while traditional learning relies on low-end devices. Therefore, deep learning requires the inclusion of a GPU. It’s an integral part of what it does. They also require a lot of matrix multiplication.

C. Functional engineering

This is a universal process. Here, domain knowledge is used to create feature extractors to reduce the complexity of the data and make patterns more visible to how the learning algorithm works, although it is very difficult to process. Therefore, it is time consuming and requires expertise.

[D]. Ways to solve the problem

In general, we use traditional algorithms to solve problems. But it needs to break the problem down into different parts to solve them individually. To get results, combine them all together.

Such as:

Let’s say you have a multi-object detection task. In this task, we must determine what the object is and its position in the image. In machine learning, we must divide the problem into two steps:

• Object detection

• Object recognition

First, we use a crawl algorithm to traverse the image and find all possible objects. Then, among all the identified objects, you will use object recognition algorithms such as SVM and HOG to identify the relevant objects.

E. Execution time

In general, deep learning requires more time to train than machine learning. The main reason is that there are too many parameters in the deep learning algorithm. Machine learning requires less time to train, ranging from a few seconds to a few hours.

F. Interpretability

We use interpretability as a factor in comparing the two learning techniques. Still, deep learning is being considered twice before industrial applications.

Where are machine learning and deep learning mainly used?

A. Computer vision: We use it for applications like license plate recognition and facial recognition.

B. Information retrieval: we use ML and DL for applications such as search engines that include text retrieval and image retrieval.

C. Marketing: We use these learning techniques for automated email marketing and customer identification.

D. Medical diagnosis: It also has a wide range of applications in the medical field, such as cancer recognition and abnormal detection.

• Natural language processing

• For similar sentiment analysis, photo tag generation, online advertising and other applications

Learn more about machine learning applications here.

The future trend

• Machine learning and data science are a trend these days. Demand for both products is growing rapidly among companies. Both are sorely needed by companies that want to incorporate machine learning into their businesses.

• Deep learning is found and proven to have the best technical expressiveness. And deep learning continues to amaze us and will continue to do so in the near future.

• In recent years, researchers have been exploring machine learning and deep learning. In the past, researchers were confined to academia. Today, however, ML and DL have their place in industry and academia.

conclusion

We have discussed and compared deep learning and machine learning. We also studied images for better expression and understanding. If you have any questions, please post them in the comments section.

From the website: Open Source China