HackerNoon

The Anatomy of AI: A Developer's Field Guide to the Internals

A deep technical guide to how modern AI really works—from neural networks and transformers to RAG, embeddings, reasoning ...
The Hans India

Why the Future of AI Engineering Begins With Mathematics, Not Just Machine Learning Tools

In recent years, artificial intelligence has become more accessible than ever before. Powerful libraries, automated platforms, and pre-trained models allow developers to build complex AI systems with ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
IEEE

Unveiling Illusionary Robust Features: A Novel Approach for Adversarial Defenses in Deep Neural Networks

Abstract: Deep Neural Networks (DNNs) are vulnerable to visually imperceptible perturbations, known as Adversarial Examples (AEs). The leading hypothesis attributes this susceptibility to “non-robust ...
marktechpost

Understanding the Artificial Neural Networks ANNs

Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Modeled after the human brain, ANNs enable machines to learn ...
IEEE

An Efficient Inversion Framework for Audio-Magnetotellurics With Borehole Constraints Combining Supervised Descent Method and Gaussian Distribution Modeling Strategy

Abstract: In audio-magnetotellurics (AMT) inversion, the resistivity model derived from data is crucial for understanding geological properties. Current AMT inversion methods such as Gaussian–Newton ...
PNAS

On the different regimes of stochastic gradient descent

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