New research from the University of St Andrews, the University of Copenhagen and Drexel University has developed AI ...

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

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

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Guitarists today are spoiled for choice, and that goes doubly true for players who use computer-based amp modeling software. I’m one such player, and I don’t miss the size, weight, deafening volume, ...

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

Abstract: This article presents the development, implementation, and validation of a loss-optimized and circuit parameter-sensitive triple-phase-shift (TPS) modulation scheme for a dual-active-bridge ...

The current machine_learning directory in TheAlgorithms/Python lacks implementations of neural network optimizers, which are fundamental to training deep learning models effectively. To fill this gap ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.

This study presents valuable computational findings on the neural basis of learning new motor memories without interfering with previously learned behaviours using recurrent neural networks. The ...

Abstract: Among the various control strategies for complex dc–dc converters, those employing neural networks have risen to prominence. They excel in their ability to approximate functions without the ...