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

In most discussions, deep learning means using deep neural networks. There are, however, a few algorithms that implement deep learning using other kinds of hidden layers besides neural networks ...

Deep learning works by using multilayered neural networks. To make sense of the data they are fed, such as photos, neural networks pass it through an interconnected layer of nodes beginning with ...

While deep neural networks are all the rage, the complexity of the major frameworks has been a barrier to their use for developers new to machine learning. There have been several proposals for ...

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved ...

Deep learning is a subset of machine learning which uses neural networks to perform learning and predictions. Deep learning has shown amazing performance in various tasks, whether it be text, time ...

Deep learning is often compared to the brains of humans and animals.However, the past years have proven that artificial neural networks, the main component used in deep learning models, lack the ...

8 practical examples of deep learning. Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling?

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.