Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Tight PPA constraints are only one reason to make sure an NPU is optimized; workload representation is another consideration.

If you use consumer AI systems, you have likely experienced something like AI "brain fog": You are well into a conversation ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

When managing associate Tanya Sadoughi found a recurring problem in the banking and finance practice, she put her newfound ...

Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...

Understand and implement the RMSProp optimization algorithm in Python. Essential for training deep neural networks efficiently. #RMSProp #Optimization #DeepLearning ...

Abstract: The processing-in-memory architecture based on memristors has been widely studied for hardware implementation in neural networks, serving as a solution to address the von Neumann bottleneck.