Deep Learning vs. Machine Learning vs. Pattern Recognition

Introduction:

Figure 1: An algorithm to detect the character “3” using sub-blocks

Three Popular Terms Correlated with “Learning”

  • Starting from 2010, machine learning is steadily becoming popular again.
  • Pattern recognition used to be the hottest topic at the very beginning of the graph but is steadily declining.
  • Deep learning is a new and fast-rising area, beating the popularity of pattern recognition in 2015.

Figure 2: The Google search index of the three concepts since 2004 (Picture source: Google Trends)

Pattern Recognition: The Beginning of Intelligent Programs

It is appropriate to say that pattern recognition was the most innovative and “intelligent” signal processing of the 1970s, the 1980s, and even the early 1990s. Concepts such as decision tree, heuristic method, and quadratic discriminatory analysis were all introduced during this period. Pattern recognition slowly shifted from being a topic in electrical engineering to a topic of interest in computer science. One of the most famous books in pattern recognition, Pattern Classification by Duda and Hart, was released in 1973. Despite being published more than four decades ago, it is still a good introductory textbook for beginners seeking to know more about the pattern recognition field.

Machine Learning: Intelligent Programs that Learn from Samples

Figure 3: Typical machine learning process (Picture source: Dr. Natalia Konstantinova’s blog).

In the middle of the 21st century, machine learning has emerged as an important research topic in computer science. Scientists have begun applying the concept broadly, creating new businesses using this technology. Machine learning has been used in robotics, genetic analysis, and in predictions for the financial market. Moreover, machine learning’s combination with the graph theory created a new topic of research — the graph model.

Machine learning has become a basic skill for many people, but it has also caused a lot of confusion especially to people new to this field. We have seen a wide variety of methods and schools of thoughts in the machine-learning field, all having its own benefits.

Deep Learning: A Framework to Unite the World

Figure 4: ConvNet framework (Picture source: Torch’s textbook)

In deep learning, there is minimal human intervention and bias because the parameters in the modes are learned from statistics. However, deep learning is only possible with an ample amount of statistics (big data) and strong arithmetic capabilities (graphic processor or GPU) to optimize the mode. Because convolution computation has been widely applied in computer vision, it is the natural choice for the mode of deep learning.

To understand deep learning, you should have a basic knowledge of linear algebra and programming. If you are not familiar with these topics, we strongly recommend Andrej Karpathy’s blog “Hacker’s Guide to Neural Networks.

Despite the benefits of deep learning, there are still many unsolved questions in its application. There are no existing theories concerning the validity of deep learning, nor textbooks on specific guidelines of deep learning. There have also been valid concerns for the possibility of artificial intelligence taking over jobs through deep learning. However, successful implementation of deep learning and artificial intelligence still requires plenty of human intervention. A high-quality product requires great vision, expertise of the field, market development, and most of all, the creativity of human beings.

Additional Relevant Technical Terms

  • Artificial Intelligence: is the oldest as well as the most encompassing technical term. Artificial intelligence is sometimes used to describe all topics related to learning, and its popularity has fluctuated in the past 50 years. In simple terms, artificial intelligence is the potential of a computer program or a device to think, learn, and interact with a human user. It is widely applied in fields such as healthcare, robotics, and finance.

Conclusion

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