The grandfather of the modern neural net field is Geoffrey Hinton from the University of Toronto (now at Google). He taught a Coursera class in 2012; it is a bit dated, but he gives such beautiful ...

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

Thanks to Neural Networks and Deep Learning, jobs and capabilities that were once considered the forte of humans are now being performed by machines. Today, Machines are no longer made to eat more ...

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.

The 2012 breakthrough—the deep learning revolution—was the discovery that we can get dramatically better performance out of neural networks with not just a few layers but with many.

Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of ...

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically ...

The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on.

The term deep neural network can have several meanings, but one of the most common is to describe a neural network that has two or more layers of hidden processing neurons. This article explains how ...