Cyber With Debra!

Artificial intelligence includes many concepts that build on one another. One term that often comes up alongside machine learning is deep learning. The two are closely related, but they are not the same thing.

What it means
Deep learning is a type of machine learning that uses neural networks with multiple layers to learn patterns from data.

A neural network is a computational model made up of connected units that process information. When a neural network has many layers between the input and output, it can learn increasingly complex features and relationships within data. This use of multiple layers is where the “deep” in deep learning comes from. For example, when analyzing an image, earlier layers might learn to detect simple features such as edges and shapes. Later layers can combine those features to recognize more complex things, such as objects or faces.

How is deep learning different from machine learning?
Deep learning is actually a subset of machine learning. Traditional machine learning often relies more heavily on people to identify or select the features in data that a model should use. Deep learning can learn many of those features automatically from large amounts of data.

This makes deep learning particularly useful for working with complex information such as images, speech, video, and language. However, deep learning also typically requires large amounts of data and significant computing power to train effectively.

Why it matters
Deep learning is behind many AI capabilities we encounter today.
It is used in areas such as speech recognition, image recognition, language translation, autonomous systems, and generative AI. Large Language Models, which we explored previously, also rely on deep learning techniques to process and generate language.

Understanding this relationship helps put the different pieces of AI together. Artificial intelligence is the broader field. Machine learning is one approach within AI, and deep learning is a specialized approach within machine learning.

Everyday takeaway
Deep learning does not replace machine learning. It is part of it. By using neural networks with multiple layers, deep learning systems can learn complex patterns from large amounts of data and power many of the AI capabilities we use today. So, the next time you hear the term “deep learning,” remember that the name gives us a clue: it is machine learning that uses deeper, multilayered neural networks to learn from data.

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