来自MSN

DenseNet Architecture Explained | Beginner’s Deep Learning Tutorial

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.
IEEE

An Ensemble Approach: Stacked Pre-Trained Deep Learning Based and Logistic Regression Meta ...

Abstract: Breast cancer is a prominent and fatal cancers that affect adult females worldwide. Improving patient outcomes requires early detection and accurate diagnosis. Recent advances in computer ...
Scientific Research Publishing

MathWorks (2024) Train Regression Models in Regression Learner App.

ABSTRACT: Facing increased energy demand which surpasses national grid supply capacity due to rapid population growth, urbanization, and economic activities, developing countries such as Cameroon are ...
The New York Times

What A.I. Really Means for Learning

A.I. is fueling a “poverty of imagination.” Here’s how we can fix it. By Meher AhmadJessica Grose and Tressie McMillan Cottom Produced by Vishakha Darbha Artificial intelligence is already showing up ...
GitHub

Learn PyTorch for Deep Learning

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...
Frontiers

Deep learning-based object detection for environmental monitoring using big data

Introduction: Recent advances in artificial intelligence have transformed the way we analyze complex environmental data. However, high-dimensionality, spatiotemporal variability, and heterogeneous ...
Frontiers

Deep learning applications for human embryo assessment using time-lapse imaging: scoping review

Background: The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment and selection in clinical in vitro Fertilization (IVF).
IEEE

Characterizing Regression in Deep Learning Model Evolution

Abstract: Deep learning (DL) models have seen extensive application across diverse fields. As these models continue to advance for enhanced predictive capabilities, instances of performance ...