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.

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 ...

Same as CSCA 5842 Specialization: Natural Language Processing: Deep Learning Meets Linguistics Instructor: Katharina Kann Prior knowledge needed: TBD View on Coursera Learning Outcomes Define ...

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.

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

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 are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research.

New learning algorithms and architectures that are currently being developed for deep neural networks will only accelerate this progress.

Artificial Neural Networks: Learning by Doing Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

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.