Originally published by Bernard Marr on LinkedIn: What Are Artificial Neural Networks - A Super-Simple Explanation For Anyone. There are many things computers can do better than humans—calculate ...

Self-Learning: Neural networks have the ability to learn on their own by analyzing data, meaning they can adapt and improve without needing explicit programming for every task.

But neural networks have a big problem: they're really complicated. They're so complex, in fact, that researchers have often struggled to explain precisely why they make specific decisions.

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.

A Nitty-Gritty Explanation of How Neural Networks Really Work See the first in a three part series that explains machine learning through math. By Sophie Weiner Published: Oct 06, 2017 1:17 PM EDT ...

Supervised learning is a type of machine learning where the data you put into the model is “labeled.” Labeled simply means that the outcome of the observation (a.k.a. the row of data) is known.

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.

A Convolutional Neural Network (CNN) represents a sophisticated advancement in artificial intelligence technology, specifically designed to process and analyze visual information.