This paper develops fundamental limits of deep neural network learning by characterizing what is possible if no constraints are imposed on the learning algorithm and on the amount of training data.

Welcome to DeepGleason, an advanced software solution developed for inferring Gleason grading in prostate whole-slide images using a deep neural network. Our API is designed to assist pathologists and ...

Python implementation of the Mixed-Scale Dense Convolutional Neural Network. [Latest Release] [Version history] [Bug Tracker] [Documentation] If you use this code in a publication, we would appreciate ...

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch. Dropout in Neural Network is a regularization technique in Deep Learning to ...

In this paper, we propose an action recognition method based on image encoding and a dual-channel feature extraction network. We convert time-series data collected from wearable sensors into color ...

A hybrid approach to AI is powering Amazon’s Rufus shopping assistant and cutting-edge warehouse robots.