What is Deep Learning?

Deep learning (DL) is a subset of machine learning (ML), which is a branch of artificial intelligence (AI). It aims to mimic how we learn and make decisions.

DL engineers create algorithms that analyze complex data, identify patterns, and make predictions or decisions, closely imitating what we do.

IBM, an American multinational technology company, says the following about the term:

“Deep learning is a subset of machine learning that uses multi-layered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) in our lives today.”

Artificial neural network – heart of deep learning

At the heart of DL is a structure inspired by the human brain called an artificial neural network.

An artificial neural network (ANN), modeled on the human brain’s neural connections, is a type of computational system. It simulates the way brain neurons interact, facilitating the processing and interpretation of complex data.

While medical doctors talk about “neurons” in the human brain, AI engineers refer to “nodes” in neural networks. The terms “neural network” and “artificial neural network” have the same meaning – we can use them interchangeably.

These networks can learn on their own and make intelligent decisions without human help. They do this by adjusting the connections based on the input data they receive. We refer to this process as “training.”

  • Training

During training, engineers feed a deep learning model a large amount of data, which is processed through the network’s layers. The layers progressively extract features and patterns relevant to the task at hand.

An image of an artificial neural network that looks like a human brain and a definition of deep learning.
Image created by market business news.

Deep learning and unstructured data

DL can handle vast amounts of unstructured data such as text, sound, and images. Deep learning models, for example, are behind the technology that powers voice assistants, identifies objects in photographs, and recommends movies on streaming platforms.

DL has advanced significantly over the past few years, thanks to improvements in computing power and the availability of large datasets to train models.

Examples of DL in action today

Recent advances in deep learning have sparked innovations across various sectors. Here are some notable examples:

  • Healthcare

DL enables the prediction of diseases from medical images.

  • Autonomous vehicles

Self-driving vehicles use DL to interpret and navigate traffic scenes.

  • Natural language processing

Deep learning facilitates the understanding and usage of human language by machines.

  • Retail companies

Supermarkets, stores, and online retails businesses use DL for personalized shopping experiences, predicting customer preferences from their browsing, and purchase history.

  • Finance

Banks and other financial institutions use algorithms for fraud detection and analyzing transaction patterns to identify suspicious activities.

Deep learning mimics human learning

The essence of deep learning is its ability to learn ‘on the go,’ that is, its ability to learn from data in a way that mimics how we learn. However, DL does this at a scale and speed that humans cannot match.

DL applications continue to expand their reach, offering innovative solutions to complex challenges, and enhancing technologies that enrich our daily lives.

Conclusion

Deep learning is a powerful machine learning technique that uses artificial neural networks to process and learn from large amounts of data. Today, its impact is evident in our daily lives and in nearly every industry.