Difference between machine learning vs deep learning, why more people choose machine learning

Contrasive introduction to deep learning and machine learning

a. What is machine learning?

Usually, in order to implement artificial intelligence, we use machine learning. We have several algorithms for machine learning. E.g:

• Find-S

• Decision trees

• Random forests

• Artificial Neural Networks

Usually, there are 3 types of learning algorithms:

• Supervise machine learning algorithms for prediction. In addition, the algorithm searches for patterns in value tags assigned to data points.

• Unsupervised machine learning algorithm: no tags are associated with data. Also, these ML algorithms cluster data into clusters. In addition, he needs to describe its structure and make complex data look simple and organized.

• 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 the strategy to learn better.

b. What is deep learning?

Machine learning focuses only on solving real-world problems. It also requires some ideas for artificial intelligence. Machine learning uses neural networks designed to mimic human decision-making capabilities. ML tools and techniques are two major narrow subsets that focus only on deep learning. We need to apply it to solve any problem that needs to be considered - human or artificial. Any deep neural network will contain the following three layers:

• Input layer

• Hidden layer

• Output layer

We can say that deep learning is the latest term in the field of machine learning. This is a way to implement machine learning.

3. Deep learning vs machine learning

We use machine algorithms to parse data, learn data, and make sensible decisions. Fundamentally, deep learning is used to create artificial "neural networks" that are self-learning and sensible. We can say that deep learning is a subfield of machine learning.

4. Comparison of machine learning and deep learning

a. Data dependency

Performance is the most important difference between the two. When the amount of data is small, the deep learning algorithm does not perform well. This is the only reason why the DL algorithm requires a lot of data to understand.

We can see that the algorithm created by the artificially created scene has the upper hand. The above picture summarizes the situation.

b. Hardware dependencies

In general, deep learning relies on high-end devices, while traditional learning relies on low-end devices. Therefore, deep learning requires a GPU. This is an integral part of its work. They also require a large number of matrix multiplication operations.

c. Functional engineering

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

d. Ways to solve the problem

Usually, we use traditional algorithms to solve the problem. But it needs to break the problem down into different parts to solve them separately. To get the results, combine them all.

E.g:

Let us assume that you have a multi-object detection task. In this task, we have to determine what the object is and where it is in the image. In the machine learning method, we must divide the problem into two steps:

• Object detection

• Object recognition

First, we use a grab 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 related 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. Interpretable

We use interpretability as a factor in comparing the two learning techniques. Despite this, deep learning is still considered before industrial applications.

Where is machine learning and deep learning applied?

1. Computer Vision: We use it for applications like license plate recognition and facial recognition.

2. Information Retrieval: We use ML and DL for applications such as search engines that include text retrieval and image retrieval.

3. Marketing: We use these learning techniques in automated email marketing and customer segment identification.

4. Medical diagnosis: It is also widely used in the medical field, such as cancer identification and abnormal detection applications.

• Natural language processing

• For applications like sentiment analysis, photo tag generation, online advertising, etc.

Learn more about machine learning applications here.

Future trend

• Machine learning and data science have become a trend today. In the enterprise, the demand for these two products is growing rapidly. For companies that want to incorporate machine learning into their business, both are in desperate need.

• Deep learning is discovered and proven to have the best technical expression. And, deep learning is constantly giving us surprises and will continue to do so in the near future.

• In recent years, researchers have continued to explore machine learning and deep learning. In the past, researchers were limited to academia. However, today, both ML and DL have their own place in industry and academia.