BIOENGINEER

Puma-Inspired Algorithm Sharpens Feature Selection for Machine Learning

Researchers at Yazd University developed a binary version of the Puma Optimization Algorithm that achieved 91.53 percent ...
Nature

Optimization Techniques in Machine Learning Applications

Optimization lies at the heart of machine learning, governing how models learn from data, tune internal parameters and adapt to new tasks. At its core, optimisation ...
Unite.AI

SGD vs. Adam: How Machine Learning Optimizers Actually Learn

Stochastic gradient descent and Adam are optimization algorithms that update model parameters from estimated gradients, but ...
BIOENGINEER

Deep Learning Meets Multi-Objective Optimization to Grade Vocational Teaching Quality

A new hybrid AI framework combining deep convolutional networks with multi-objective evolutionary optimization achieves 98.5 ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

4 Books That Bridge the Gap Between Machine Learning Theory and Implementation

Introduction A few years ago, I took over a demand forecasting model from a colleague who had left the company. The notebook ...
HackerNoon

Building a Production CI/CD Pipline for Machine Learning Models Across Distributed Industrial Plants

I ML deployment is different from cloud CI/CD. Learn how site-aware validation, versioned models, staged rollouts, and ...