Key Takeaways -   To understand data science, one needs a lot of technical expertise along with business understanding. Generative AI, MLOps, and clou ...

Gradient descent has a fundamental limitation: on most real-world loss surfaces, it is inefficient. When the surface has uneven curvature—steep in one direction and flat in another, which is common in ...

As modern computing becomes limited by energy consumption, there is growing interest in physical computing paradigms that can operate closer to fundamental thermodynamic limits. Thermodynamic ...

Abstract: A fast gradient-descent (FGD) method is proposed for far-field pattern synthesis of large antenna arrays. Compared with conventional gradient-descent (GD) methods for pattern synthesis where ...

The success of deep learning contrasts with its limited understanding. One example is stochastic gradient descent, the main algorithm used to train neural networks ...

A background illustration showing a watercolor painting with many overlapping people. As the page scrolls, the illustration transitions into irregular shaped circles of various colors that sometimes ...

This article illustrates how to build, in less than 5 minutes, a simple linear regression model with gradient descent. The goal is to predict a dependent variable (y) from an independent variable (X).

Abstract: In this paper, the projected-gradient-descent (PGD) -based detector for massive MIMO system, which consists of two basic operations — projection and gradient descent (GD), is studied to ...