In this course, we’ll examine the history of neural networks and state-of-the-art approaches to deep learning. Students will learn to design neural network architectures and training procedures via ...

Neural Networks and Deep Learning have lent enormous success to researchers in tasks such as image recognition, speech recognition, and finding deeper relations in data sets.

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.

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 ...

Andrew Y. Ng, co-founder of Coursera, talks in a crowded lecture hall on Thursday at Maxwell Dworkin about deep learning in computer programs, inspired by human neural networks.

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

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.

This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.