In this video, we will understand in detail what is Momentum Optimizer in Deep Learning. Momentum Optimizer in Deep Learning ...

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test ...

The Center for Deep Learning’s (CDL) mission is to act as a resource for companies seeking to establish or improve access to artificial intelligence (AI) by providing technical capacity and expertise, ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly accessible ...

Deep learning modeling that incorporates physical knowledge is currently a hot topic, and a number of excellent techniques have emerged. The most well-known one is the physics-informed neural networks ...

Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...

Machine learning workloads require large datasets, while machine learning workflows require high data throughput. We can optimize the data pipeline to achieve both. Machine learning (ML) workloads ...

Background Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains ...