What’s The Difference Between Neural Networks and Deep Learning

Artificial intelligence (AI) and machine learning (ML) are terms that are often used interchangeably. In reality, however, ML is a subset of AI. Within ML, neural networks and deep learning are two core components. So, what exactly are neural networks and deep learning?

Neural networks refer to a set of algorithms inspired by the structure and function of the human brain. They are designed to recognize patterns and classify data. Deep learning, on the other hand, is a more complex neural network that is capable of handling large, unstructured data sets.

While neural networks and deep learning share some similarities, deep learning is a more sophisticated and complex system. It is capable of handling more complex problems and is suited for use with unstructured data. Neural networks, on the other hand, are better suited for structured data and can be applied to a wide range of problems.

If you're looking to dive deeper into the world of AI and ML, having a solid understanding of the differences between neural networks and deep learning is essential. By understanding these core components, you'll be better equipped to navigate the rapidly evolving landscape of AI and drive meaningful change with your work.

The post What’s The Difference Between Neural Networks and Deep Learning originally appeared on Master Data Skills + AI.