Machine Learning vs. Deep Learning

Artificial intelligence (AI) continues to revolutionize the world. As technology becomes more advanced and the amount of data collected increases, the need for this technology will further increase. Furthermore, several branches of fields have emerged from AI due to its complexities and reach. The two main types of AI algorithms are machine learning and deep learning. In general, most people interchange and confuse these two terms. However, each term is separate, and it operates differently. As such, this article will differentiate between machine learning and deep learning.

Definition of Machine Learning

The term machine learning refers to computer learning and adaptation using data. It encapsulates an intersection between statistics and computer science. Therefore, it uses algorithms to perform tasks without a fixed program. The process of performing these tasks is done via the recognition of patterns within and the use of prediction tools to make decisions and conclusions. Generally, machine learning algorithms can be unsupervised or supervised. The type used depends on the nature of the available data.

Definition of Deep Learning

Deep learning is a sophisticated version of machine learning. It is based on the mathematical evolution of algorithms within the machine learning framework. As a result, many researchers are throwing their weight on deep learning due to its potential. Deep learning algorithms use logic structures to analyze data in the same way humans make decisions and conclusions. Like machine learning, deep learning algorithms can be unsupervised and supervised. The most common type of deep learning algorithm that is used is the artificial neural network.

Differences Between Machine Learning and Deep Learning

There are several differences between these two artificial intelligence technologies. Some of the most common differences are highlighted below.

Machine learning is an artificial intelligence algorithm that allows a computer to make decisions or arrive at conclusions using data. Deep learning, on the other hand, is an advanced version of machine learning. Its algorithms use human-like logic to reach conclusions and make decisions.
Machine learning algorithms are simple and possess a simple structure like decision trees. However, deep learning is complex and works like the human brain. Therefore, its structure is far more complex and looks more like a web.
Machine learning algorithms may require a lot of human intervention to operate. Conversely, deep learning algorithms need little human intervention. The self-driving car is an example of a technology that uses deep learning and requires little to no human intervention.
Machine learning can function with ‍a few data points. However, deep learning requires a lot of data points to operate at an optimal rate. this data is required to build a complex layer of the structure that allows the algorithm to operate.
A software specialist is needed when identifying features in a machine learning setup. As such, machine learning does not offer an intuitive look into its process. With deep learning, a software expert is not required for the identification of features. features are automatically identified through the neural network.

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