Why “Neural Network” and “Deep Learning” Are Actually the Same Thing

Many people wonder whether neural networks and deep learning are different concepts. Online sources often describe neural networks as a branch of machine learning and deep learning as a further specialization of neural networks, implying a hierarchical relationship.

In reality, this explanation is a post‑hoc reinterpretation of the names rather than their true origin. Understanding the history of AI development shows that the two terms refer to the same thing.

Early research used the term “Neural Network” directly, as seen in original papers that employed the English phrase “Neural Network.” However, at that time many experts resisted neural‑network research, and limited data and computing power prevented significant breakthroughs, so the field stagnated.

Because the term became associated with failed research, it acquired a negative reputation; both ordinary people and scholars began to dislike it. Similar to today’s reaction to “AI,” any work labeled with the term faced skepticism unless it was exceptionally well done.

When neural‑network research finally made progress, the term was avoided in publications—papers mentioning “neural network” were sometimes rejected. In 2006 Geoffrey Hinton renamed his work “Deep Belief Network (DBN),” which later became widely known as deep learning.

After the name change, the negative connotation faded, but real breakthroughs still depended on genuine technical advances. Hinton later won a major image‑recognition competition using deep‑learning methods, dramatically outperforming traditional machine‑learning approaches, which reignited interest in the field.

Since then, whether called neural networks or deep learning, the terminology no longer matters; both represent a promising research direction.