The Difference Between Deep Learning And Machine Learning

As a relatively new term, machine learning is often associated with other terms such as deep learning, artificial intelligence, data mining, machine learning datasets, data science, and expert systems. Are all those words the same thing? Or is it different? The difference between machine learning and deep learning lies in the aspects of scope, data, goals, processing, human intervention, and computing needs. Deep learning is part of machine learning, where deep learning uses algorithms that mimic the way humans work, namely artificial neural networks and their derivatives.

Judging from its understanding, Machine learning is an applied branch of Artificial Intelligence with a focus on developing a system that can learn on its own without having to be repeatedly programmed by humans. While deep learning is part of machine learning. Deep Learning is one of the methods of machine learning using an algorithm that imitates the way humans work, namely an artificial neural network and its derivatives.

Similar to the way humans learn from experiences, deep learning algorithms will “learn” over and over again to increase the accuracy of their results. The difference between Machine Learning and Deep Learning is as follows.

1. Scope
Machine Learning is a broader concept. Deep Learning is part of Machine Learning.

2. Data
Although both require sufficient data, Deep Learning is usually carried out on more data, and the results are not good enough if there is little data.

3. Process
Machine Learning uses algorithms to dig into data, learn from that data, and make the right decisions based on what it has learned. Whereas Deep Learning builds algorithms in layers to create an “artificial neural network”, a structure that resembles the human brain, which can learn and make “intelligent” decisions on its own.

4. Algorithm
Machine Learning has a simple (though not all) structure, such as linear regression or decision trees. While Deep Learning has an artificial neural network structure. These multi-layered structures, like the human brain, are complex and interrelated.