In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such ...

Neural networks made from photonic chips can be trained using on-chip backpropagation – the most widely used approach to training neural networks, according to a new study. The findings pave the way ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

The hype over Large Language Models (LLMs) has reached a fever pitch. But how much of the hype is justified? We can't answer that without some straight talk - and some definitions. Time for a ...

Neural networks have emerged as powerful tools in the field of neutron spectrometry and dosimetry by offering non-linear, data‐driven approaches to reconstruct complex neutron energy spectra and ...

For more than eighty years, deep learning has relied on a simplified model of brain function. Now, a Pittsburgh startup ...

AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling ...