Deep Learning with Yacine on MSN

Backpropagation from scratch in Python – step by step neural network tutorial

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...
Deep Learning with Yacine on MSN

AdamW optimizer from scratch in Python – step-by-step tutorial

Build the AdamW optimizer from scratch in Python. Learn how it improves training stability and generalization in deep learning models. #AdamW #DeepLearning #PythonTutorial ...
Ministry of Testing

Back to the basics: Rethinking how we use AI in testing

A simple rule of thumb: In general, AI is best reserved for well-defined, repetitive tasks. This includes anything that ...
eLife

Separating selection from mutation in antibody language models

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...
IEEE

Cardiac Output Quantification via Esophageal Photoplethysmography and Deep Learning

Abstract: Continuous minimally invasive cardiac output (CO) measurement via photoplethysmography (PPG) has received much attention in daily monitoring. However, the precision of current research is ...
TMCnet

Bridging Silence: A Real-Time Sign Language to English Text Translation System Using Python, OpenCV, and Convolutional Neural Networks

Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive technology and inclusive education. In an attempt to close that gap, I developed a ...
GitHub

EdgeTrain: Automated Resource Adjustment for Efficient Edge AI Training

EdgeTrain is a Python package designed to dynamically adjust deep learning training parameters and strategies based on CPU and GPU performance. It optimizes the training process by adjusting batch ...
VentureBeat

Ai2's new Olmo 3.1 extends reinforcement learning training for stronger reasoning benchmarks

The Allen Institute for AI (Ai2) recently released what it calls its most powerful family of models yet, Olmo 3. But the company kept iterating on the models, expanding its reinforcement learning (RL) ...
IEEE

Near-Field Beam Training and Tracking With Deep Learning for Extremely Large-Scale MIMO

Abstract: In this paper, we consider beam training and tracking for extremely large-scale multiple-input multiple-output communications. A convolutional neural network (CNN) assisted two-stage ...
Oak Ridge National Lab

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...
InfoQ

Dreamer 4: Learning to Achieve Goals from Offline Data through Imagination Training

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...